Details of all papers

My research focuses on Artificial Intelligence as an autonomous creator or as a facilitator of human creativity. My work includes computationally intelligent tools for game design, as well as computational creators that blend semantics, visuals, sound, plot and level structure to create horror games, adventure games and more. You can find details of all my papers below, ordered from most to least recent.

Modelling the Quality of Visual Creations in Iconoscope

Antonios Liapis, Daniele Gravina, Emil Kastbjerg and Georgios N. Yannakakis

Abstract: This paper presents the current state of the online game Iconoscope and analyzes the data collected from almost 45 months of continuous operation. Iconoscope is a freeform creation game which aims to foster the creativity of its users through diagrammatic lateral thinking, as users are required to depict abstract concepts as icons which may be misinterpreted by other users as different abstract concepts. From users' responses collected from an online gallery of all icons drawn with Iconoscope, we collect a corpus of over 500 icons which contain annotations of visual appeal. Several machine learning algorithms are tested for their ability to predict the appeal of an icon from its visual appearance and other properties. Findings show the impact of the representation on the model's accuracy and highlight how such a predictive model of quality can be applied to evaluate new icons (human-authored or generated).

in Proceedings of the 8th International Games and Learning Alliance Conference. Springer, 2019. BibTex

A Participatory Approach to Redesigning Games for Educational Purposes

Stamatia Savvani and Antonios Liapis

Abstract: Even though games designed for educational purposes can be motivating, they usually shelter dated pedagogies, passive learning procedures, and often overlook learners' creativity. In an effort to reinforce the active participation of learners in games, this paper presents a participatory process in which students and teachers are involved in game design. The proposed process concerns redesigning existing commercial games into educational ones and includes establishing the learning goals, identifying appropriate commercial games, adapting the rules and context, crafting and playtesting the game. Using language learning as one application of this process, the paper presents how three well-known tabletop games were redesigned in a foreign language classroom with elementary and intermediate English language learners. The benefits that underlie the process concern students' active participation, boosting their problem-solving skills, and engaging them in creative learning.

in Proceedings of the 8th International Games and Learning Alliance Conference. Springer 2019. BibTex

Using Dates as Contextual Information for Personalized Cultural Heritage Experiences

Ahmed Dahroug, Andreas Vlachidis, Antonios Liapis, Antonis Bikakis, Martin Lopez-Nores, Owen Sacco and Jose Juan Pazos-Arias

Abstract: We present semantics-based mechanisms that aim to promote reflection on cultural heritage by means of dates (historical events or annual commemorations), owing to their connections to a collection of items and to the visitors’ interests. We argue that links to specific dates can trigger curiosity, increase retention and guide visitors around the venue following new appealing narratives in subsequent visits. The proposal has been evaluated in a pilot study on the collection of the Archaeological Museum of Tripoli (Greece), for which a team of humanities experts wrote a set of diverse narratives about the exhibits. A year-round calendar was crafted so that certain narratives would be more or less relevant on any given day. Expanding on this calendar, personalised recommendations can be made by sorting out those relevant narratives according to personal events and interests recorded in the profiles of the target users. Evaluation of the associations by experts and potential museum visitors shows that the proposed approach can discover meaningful connections, while many others that are more incidental can still contribute to the intended cognitive phenomena.

in SAGE Journal of Information Science, 2019 (accepted). BibTex

A Multi-Faceted Surrogate Model for Search-based Procedural Content Generation

Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper proposes a framework for the procedural generation of level and ruleset components of games via a surrogate model that assesses their quality and complementarity. The surrogate model combines level and ruleset elements as input and gameplay outcomes as output, thus constructing a mapping between three different facets of games. Using this model as a surrogate for expensive gameplay simulations, a search-based generator can adapt content towards a target gameplay outcome. Using a shooter game as the target domain, this paper explores how parameters of the players' character classes can be mapped to both the level’s representation and the gameplay outcomes of balance and match duration. The surrogate model is built on a deep learning architecture, trained on a large corpus of randomly generated sets of levels, classes and simulations from gameplaying agents. Results show that a search-based generative approach can adapt character classes, levels, or both towards designer-specified targets. The model can thus act as a design assistant or be integrated in a mixed-initiative tool. Most importantly, the combination of three game facets into the model allows it to identify the synergies between levels, rules and gameplay and orchestrate the generation of the former two towards desired outcomes.

in Transactions on Games, 2019 (accepted). BibTex

From Pixels to Affect: A Study on Games and Player Experience

Konstantinos Makantasis, Antonios Liapis and Georgios N. Yannakakis

Abstract: Is it possible to predict the affect of a user just by observing her behavioral interaction through a video? How can we, for instance, predict a user's arousal in games by merely looking at the screen during play? In this paper we address these questions by employing three dissimilar deep convolutional neural network architectures in our attempt to learn the underlying mapping between video streams of gameplay and the player's arousal. We test the algorithms in an annotated dataset of 50 gameplay videos of a survival shooter game and evaluate the deep learned models' capacity to classify high vs low arousal levels. Our key findings with the demanding leave-one- video-out validation method reveal accuracies of over 78% on average and 98% at best. While this study focuses on games and player experience as a test domain, the findings and methodology are directly relevant to any affective computing area, introducing a general and user-agnostic approach for modeling affect.

in Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 2019. BibTex

PAGAN: Video Affect Annotation Made Easy

David Melhart, Antonios Liapis and Georgios N. Yannakakis

Abstract: How could we gather affect annotations in a rapid, unobtrusive, and accessible fashion? How could we still make sure that these annotations are reliable enough for data-hungry affect modelling methods? This paper addresses these questions by introducing PAGAN, an accessible, general-purpose, online platform for crowdsourcing affect labels in videos. The design of PAGAN overcomes the accessibility limitations of existing annotation tools, which often require advanced technical skills or even the on-site involvement of the researcher. Such limitations often yield affective corpora that are restricted in size, scope and use, as the applicability of modern data-demanding machine learning methods is rather limited. The description of PAGAN is accompanied by an exploratory study which compares the reliability of three continuous annotation tools currently supported by the platform. Our key results reveal higher inter-rater agreement when annotation traces are processed in a relative manner and collected via unbounded labelling.

in Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 2019. BibTex

PyPLT: Python Preference Learning Toolbox

Elizabeth Camilleri, Georgios N. Yannakakis, David Melhart and Antonios Liapis

Abstract: There is growing evidence suggesting that subjective values such as emotions are intrinsically relative and that an ordinal approach is beneficial to their annotation and analysis. Ordinal data processing yields more reliable, valid and general predictive models, and preference learning algorithms have shown a strong advantage in deriving computational models from such data. To enable the extensive use of ordinal data processing and preference learning, this paper introduces the Python Preference Learning Toolbox. The toolbox is open source, features popular preference learning algorithms and methods, and is designed to be accessible to a wide audience of researchers and practitioners. The toolbox is evaluated with regards to both the accuracy of its predictive models across two affective datasets and its usability via a user study. Our key findings suggest that the implemented algorithms yield accurate models of affect while its graphical user interface is suitable for both novice and experienced users.

in Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 2019. BibTex

Procedural Content Generation through Quality-Diversity

Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.

in Proceedings of the IEEE Conference on Games, 2019. BibTex

Two-step Constructive Approaches for Dungeon Generation

Michael Cerny Green, Ahmed Khalifa, Athoug Alsoughayer, Divyesh Surana, Antonios Liapis and Julian Togelius

Abstract: This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps, initially producing the architectural layout of the level as its walls and floor tiles, and then furnishing it with game objects representing the player's start and goal position, challenges and rewards. Three layout creators and three furnishers are introduced in this paper, which can be combined in different ways in the two-step generative process for producing diverse dungeons levels. Layout creators generate the floors and walls of a level, while furnishers populate it with monsters, traps, and treasures. We test the generated levels on several expressivity measures, and in simulations with procedural persona agents.

in Proceedings of the FDG Workshop on Procedural Content Generation, 2019. BibTex

Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division

David Melhart, Ahmad Azadvar, Alessandro Canossa, Antonios Liapis and Georgios N. Yannakakis

Abstract: Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.

in Proceedings of the IEEE Conference on Games, 2019. BibTex

The Newborn World: Guiding Creativity in a Competitive Storytelling Game

Antonios Liapis

Abstract: This paper presents The Newborn World, a co-located multiplayer game for mobile devices with a focus on storytelling. The game has been designed following patterns of analog games that prompt players to tell stories, and uses digital representations of cards and tokens for interaction. The collaborative nature of storytelling is interspersed with competitive elements such as hidden goals and secret voting for best story. The paper exposes several design decisions for balancing goal-oriented and freeform play, competition and collaboration, as well as elements that constrain or foster players' creativity.

in Proceedings of the IEEE Conference on Games, 2019. BibTex

Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes

Antonios Liapis, Daniel Karavolos, Konstantinos Makantasis, Konstantinos Sfikas and Georgios N. Yannakakis

Abstract: Which features of a game influence the dynamics of players interacting with it? Can a level's architecture change the balance between two competing players, or is it mainly determined by the character classes and roles that players choose before the game starts? This paper assesses how quantifiable gameplay outcomes such as score, duration and features of the heatmap can be predicted from different facets of the initial game state, specifically the architecture of the level and the character classes of the players. Experiments in this paper explore how different representations of a level and class parameters in a shooter game affect a deep learning model which attempts to predict gameplay outcomes in a large corpus of simulated matches. Findings in this paper indicate that a few features of the ruleset (i.e. character class parameters) are the main drivers for the model's accuracy in all tested gameplay outcomes, but the levels (especially when processed) can augment the model.

in Proceedings of the IEEE Conference on Games, 2019. BibTex

Blending Notions of Diversity for MAP-Elites

Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis

Abstract: Quality-diversity algorithms focus on discovering multiple diverse and high-performing solutions. MAP-elites is such an algorithm, as it partitions the solution space into bins and searches for the best solution possible for each bin. In this paper, multi-behavior variants of MAP-Elites are tested where the MAP-Elites grid partitions the solution space based on a certain dimension, while selection is guided by measures of diversity on another dimension. Four divergent search algorithms are tested for this selection process, targeting novelty or surprise or their combination, and their performance on a soft robot evolution task is discussed.

in Proceedings of the Genetic and Evolutionary Computation Conference, 2019. BibTex

Deciphering Generated Symbols as a Game Mechanic: The Design of Alien Transmission

Antonios Liapis

Abstract: This article describes the gameplay and the design rationale of the Alien Transmission game, where generated symbols are used as a form of communication between players in a co-operative board game. The symbols are abstract black-and-white blobs, generated by the computer via cellular automata, but players must use these symbols to convey information regarding upcoming threats. As gameplay progresses, symbols gain a meaning due to their visual associations and past gameplay experiences. The work highlights how procedural content generation can be elevated into a mechanic rather than used to randomize the initial game state.

in Seeds: The Procjam Zine 3. 2018. BibTex

Real-world Data as a Seed

Antonios Liapis

Abstract: This article proposes the use of real-world data for game generation, and describes the design priorities of DATA Agent as an example of data-driven game generation. Due to the use (and transformation) of real-world data, DATA Agent weaves a narrative of a time-travelling doppelganger murderer and a time-traveling detective intent on catching the murder in a lie about its presumed identity. DATA Agent shows how real-world data from Wikipedia, Wikimedia Commons and OpenStreetMap can be transformed into NPCs, levels, plot points and exposition dialog.

in Seeds: The Procjam Zine 3. 2018. BibTex

Quality Diversity Through Surprise

Daniele Gravina, Antonios Liapis and Georgios N. Yannakakis

Abstract: Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance between divergence and convergence during search. While quality diversity has already delivered promising results in complex problems, the capacity of divergent search variants for quality diversity remains largely unexplored. Inspired by the notion of surprise as an effective driver of divergent search and its orthogonal nature to novelty this paper investigates the impact of the former to quality diversity performance. For that purpose we introduce three new quality diversity algorithms which employ surprise as a diversity measure, either on its own or combined with novelty, and compare their performance against novelty search with local competition, the state of the art quality diversity algorithm. The algorithms are tested in a robot navigation task across 60 highly deceptive mazes. Our findings suggest that allowing surprise and novelty to operate synergistically for divergence and in combination with local competition leads to quality diversity algorithms of significantly higher efficiency, speed and robustness.

in Transactions on Evolutionary Computation, vol. 23, no 4, pp. 603-616, 2019. BibTex

Orchestrating Game Generation

Antonios Liapis, Georgios N. Yannakakis, Mark J. Nelson, Mike Preuss and Rafael Bidarra

Abstract: The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this article identifies game facet orchestration as the central challenge for AI-based game generation, discusses its dimensions and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, we propose how orchestration can be facilitated in a top-down or bottom-up fashion, we review indicative preliminary examples of orchestration, and we conclude by discussing the open questions and challenges ahead.

in Transactions on Games, vol. 11, no 1, pp. 48-68, 2019. BibTex

Capturing the Virtual Movement of Paintings: A Game and A Tool

Kalliopi Kontiza, Antonios Liapis and Joseph Padfield

Abstract: This paper presents a virtual gallery creation game that has been designed for the National Gallery of London, as part of the CrossCult project, with a multi-purpose goal. For visitors, the game allows users to virtually move paintings around, reflecting on their visit through gamification, while creating and curating their own virtual galleries. For National Gallery staff, the application allows them to simply visualise planned exhibitions and to accurately record the positions of paintings when they are moved or re-positioned. This paper describes the game's underlying structure, designed to serve both as an expert tool and as a game, and discusses the results obtained from initial experiments with end-users. Some preliminary conclusions can be drawn regarding the extent to which the application allows the end users to reflect on the National Gallery collection while creating and curating their own virtual galleries.

in Proceedings of the Digital Heritage Conference, 2018. BibTex

DATA Agent

Michael Cerny Green, Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper introduces DATA Agent, a system which creates murder mystery adventures from open data. In the game, the player takes on the role of a detective tasked with finding the culprit of a murder. All characters, places, and items in DATA Agent games are generated using open data as source content. The paper discusses the general game design and user interface of DATA Agent, and provides details on the generative algorithms which transform linked data into different game objects. Findings from a user study with 30 participants playing through two games of DATA Agent show that the game is easy and fun to play, and that the mysteries it generates are straightforward to solve.

in Proceedings of the 13th Conference on the Foundations of Digital Games, 2018. BibTex

Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model

Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets. Focusing on the shooter game genre, the paper explores how deep learning can help build a model which combines the game level structure and the game's character class parameters as input and the gameplay outcomes as output. The model is trained on a large corpus of game data from simulations with artificial agents in random sets of levels and class parameters. The model is then used to generate classes for specific levels and for a desired game outcome, such as balanced matches of short duration. Findings in this paper show that the system can be expressive and can generate classes for both computer generated and human authored levels.

in Proceedings of the FDG Workshop on Procedural Content Generation, 2018. BibTex

Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation

Jichen Zhu, Antonios Liapis, Sebastian Risi, Rafael Bidarra and G. Michael Youngblood

Abstract: Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users' needs, and we identify key open challenges.

in Proceedings of the IEEE Conference on Computational Intelligence and Games, 2018. BibTex

A Study on Affect Model Validity: Nominal vs Ordinal Labels

David Melhart, Konstantinos Sfikas, Giorgos Giannakakis, Georgios N. Yannakakis and Antonios Liapis

Abstract: The question of representing emotion computationally remains largely unanswered: popular approaches require annotators to assign a magnitude (or a class) of some emotional dimension, while an alternative is to focus on the relationship between two or more options. Recent evidence in affective computing suggests that following a methodology of ordinal annotations and processing leads to better reliability and validity of the model. This paper compares the generality of classification methods versus preference learning methods in predicting the levels of arousal in two widely used affective datasets. Findings of this initial study further validate the hypothesis that approaching affect labels as ordinal data and building models via preference learning yields models of better validity.

in Proceedings of the IJCAI workshop on AI and Affective Computing, 2018. BibTex

Using a Surrogate Model of Gameplay for Automated Level Design

Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper describes how a surrogate model of the interrelations between different types of content in the same game can be used for level generation. Specifically, the model associates level structure and game rules with gameplay outcomes in a shooter game. We use a deep learning approach to train a model on simulated playthroughs of two-player deathmatch games, in diverse levels and with different character classes per player. Findings in this paper show that the model can predict the duration and winner of the match given a top-down map of the level and the parameters of the two players' character classes. With this surrogate model in place, we investigate which level structures would result in a balanced match of short, medium or long duration for a given set of character classes. Using evolutionary computation, we are able to discover levels which improve the balance between different classes. This opens up potential applications for a designer tool which can adapt a human authored map to fit the designer's desired gameplay outcomes, taking account of the game's rules.

in Proceedings of the IEEE Conference on Computational Intelligence and Games, 2018. BibTex

Data-driven Design: A Case for Maximalist Game Design

Gabriella A. B. Barros, Michael Cerny Green, Antonios Liapis and Julian Togelius

Abstract: Maximalism in art refers to drawing on and combining multiple different sources for art creation, embracing the resulting collisions and heterogeneity. This paper discusses the use of maximalism in game design and particularly in data games, which are games that are generated partly based on open data. Using Data Adventures, a series of generators that create adventure games from data sources such as Wikipedia and OpenStreetMap, as a lens we explore several tradeoffs and issues in maximalist game design. This includes the tension between transformation and fidelity, between decorative and functional content, and legal and ethical issues resulting from this type of generativity. This paper sketches out the design space of maximalist data-driven games, a design space that is mostly unexplored.

in Proceedings of the International Conference of Computational Creativity, 2018. BibTex

Fusing Novelty and Surprise for Evolving Robot Morphologies

Daniele Gravina, Antonios Liapis and Georgios N. Yannakakis

Abstract: Traditional evolutionary algorithms tend to converge to a single good solution, which can limit their chance of discovering more diverse and creative outcomes. Divergent search, on the other hand, aims to counter convergence to local optima by avoiding selection pressure towards the objective. Forms of divergent search such as novelty or surprise search have proven to be beneficial for both the efficiency and the variety of the solutions obtained in deceptive tasks. Importantly for this paper, early results in maze navigation have shown that combining novelty and surprise search yields an even more effective search strategy due to their orthogonal nature. Motivated by the largely unexplored potential of coupling novelty and surprise as a search strategy, in this paper we investigate how fusing the two can affect the evolution of soft robot morphologies. We test the capacity of the combined search strategy against objective, novelty, and surprise search, by comparing their efficiency and robustness, and the variety of robots they evolve. Our key results demonstrate that novelty-surprise search is generally more efficient and robust across eight different resolutions. Further, surprise search explores the space of robot morphologies more broadly than any other algorithm examined.

in Proceedings of the Genetic and Evolutionary Computation Conference, 2018. BibTex

Who Killed Albert Einstein? From Open Data to Murder Mystery Games

Gabriella A. B. Barros, Michael Cerny Green, Antonios Liapis and Julian Togelius

Abstract: This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article, and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.

in Transactions on Games, vol. 11, no 1, pp. 79-89, 2019. BibTex

Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics

Christoffer Holmgard, Michael Cerny Green, Antonios Liapis and Julian Togelius

Abstract: This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo Tree Search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different play styles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.

IEEE Transactions on Games (accepted), 2018. BibTex

Piecemeal Evolution of a First Person Shooter Level

Antonios Liapis

Abstract: This paper describes an iterative process for generating multi-story shooter game levels by means of interlocking rooms evolved individually. The process is highly controllable by a human designer who can specify the entrances to this room as well as its size, its distribution of game objects and its architectural patterns. The small size of each room allows for computationally fast evaluations of several level qualities, but these rooms can be combined into a much larger shooter game level. Each room has two floors and is generated iteratively, with two stages of evolution and two stages of constructive post-processing. Experiments in generating an arena-based level for two teams spawning in different rooms demonstrate that the placement and allocation of entrances on each floor have a strong effect on the patterns of the final level.

in Applications of Evolutionary Computation. Springer, 2018. BibTex

Recomposing the Pokémon Color Palette

Antonios Liapis

Abstract: In digital games, the visual representation of game assets such as avatars or game levels can hint at their purpose, in-game use and strengths. In the Pokémon games, this is particularly prevalent with the namesake creatures' type and the colors in their sprites. To win these games, players choose Pokémon of the right type to counter their opponents' strengths; this makes the visual identification of type important. In this paper, computational intelligence methods are used to learn a mapping between a Pokémon's type and its in-game sprite, colors and shape. This mapping can be useful for a designer attempting to create new Pokémon of certain types. In this paper, instead, evolutionary algorithms are used to create new Pokémon sprites by using existing color information but recombining it into a new palette. Results show that evolution can be applied to Pokémon sprites on a local or global scale, to exert different degrees of designer control and to achieve different goals.

in Applications of Evolutionary Computation. Springer, 2018. BibTex

Mapping Chess Aesthetics onto Procedurally Generated Chess-like Games

Jakub Kowalski, Antonios Liapis and Lukasz Zarczynski

Abstract: Variants of chess have been generated in many forms and for several reasons, such as testbeds for artificial intelligence research in general game playing. This paper uses the visual properties of chess pieces as inspiration to generate new shapes for other chess-like games, targeting specific visual properties which allude to the pieces' in-game function. The proposed method uses similarity measures in terms of pieces' strategic role and movement in a game to identify the new pieces' closest representatives in chess. Evolution then attempts to minimize the distance from chess pieces' visual properties, resulting in new shapes which combine one or more chess pieces' visual identities. While experiments in this paper focus on two chess-like games from previous publications, the method can be used for broader generation of game visuals based on functional similarities of components to known, popular games.

in Applications of Evolutionary Computation. Springer, 2018. BibTex

RankTrace: Relative and Unbounded Affect Annotation

Phil Lopes, Georgios N. Yannakakis and Antonios Liapis

Abstract: How should annotation data be processed so that it can best characterize the ground truth of affect? This paper attempts to address this critical question by testing various methods of processing annotation data on their ability to capture phasic elements of skin conductance. Towards this goal the paper introduces a new affect annotation tool, RankTrace, that allows for the annotation of affect in a continuous yet unbounded fashion. RankTrace is tested on first-person annotations of tension elicited from a horror video game. The key findings of the paper suggest that the relative processing of traces via their mean gradient yields the best and most robust predictors of phasic manifestations of skin conductance.

In Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 2017. BibTex

Towards General Models of Player Affect

Elizabeth Camilleri, Georgios N. Yannakakis and Antonios Liapis

Abstract: While the primary focus of affective computing has been on constructing efficient and reliable models of affect, the vast majority of such models are limited to a specific task and domain. This paper, instead, investigates how computational models of affect can be general across dissimilar tasks; in particular, in modeling the experience of playing very different video games. We use three dissimilar games whose players annotated their arousal levels on video recordings of their own playthroughs. We construct models mapping ranks of arousal to skin conductance and gameplay logs via preference learning and we use a form of cross-game validation to test the generality of the obtained models on unseen games. Our initial results comparing between absolute and relative measures of the arousal annotation values indicate that we can obtain more general models of player affect if we process the model output in an ordinal fashion.

In Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 2017. BibTex

Game Character Ontology (GCO): A Vocabulary for Extracting and Describing Game Character Information from Web Content

Owen Sacco, Antonios Liapis and Georgios N. Yannakakis

Abstract: Creating video games that are market competent costs in time, effort and resources which often cannot be afforded by small-medium enterprises, especially by independent game development studios. As most of the tasks involved in developing games are labour and creativity intensive, our vision is to reduce software development effort and enhance design creativity by automatically generating novel and semantically-enriched content for games from Web sources. In particular, this paper presents a vocabulary that defines detailed properties used for describing video game characters information extracted from sources such as fansites to create game character models. These character models could then be reused or merged to create new unconventional game characters.

In Proceedings of the International Conference on Semantic Systems, 2017. BibTex

Learning the Patterns of Balance in a Multi-Player Shooter Game

Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis

Abstract: A particular challenge of the game design process is when the designer is requested to orchestrate dissimilar elements of games such as visuals, audio, narrative and rules to achieve a specific play experience. Within the domain of adversarial first person shooter games, for instance, a designer must be able to comprehend the differences between the weapons available in the game, and appropriately craft a game level to take advantage of strengths and weaknesses of those weapons. As an initial study towards computationally orchestrating dissimilar content generators in games, this paper presents a computational model which can classify a matchup of a team-based shooter game as balanced or as favoring one or the other team. The computational model uses convolutional neural networks to learn how game balance is affected by the level, represented as an image, and each team's weapon parameters. The model was trained on a corpus of over 50,000 simulated games with artificial agents on a diverse set of levels created by 39 different generators. The results show that the fusion of levels, when processed by a convolutional neural network, and weapon parameters yields an accuracy far above the baseline but also improves accuracy compared to artificial neural networks or models which use partial information, such as only the weapon or only the level as input.

In Proceedings of the FDG workshop on Procedural Content Generation in Games, 2017. BibTex

Modelling Affect for Horror Soundscapes

Phil Lopes, Antonios Liapis and Georgios N. Yannakakis

Abstract: The feeling of horror within movies or games relies on the audience's perception of a tense atmosphere — often achieved through sound accompanied by the on-screen drama — guiding its emotional experience throughout the scene or game-play sequence. These progressions are often crafted through an a priori knowledge of how a scene or game-play sequence will playout, and the intended emotional patterns a game director wants to transmit. The appropriate design of sound becomes even more challenging once the scenery and the general context is autonomously generated by an algorithm. Towards realizing sound-based affective interaction in games this paper explores the creation of computational models capable of ranking short audio pieces based on crowdsourced annotations of tension, arousal and valence. Affect models are trained via preference learning on over a thousand annotations with the use of support vector machines, whose inputs are low-level features extracted from the audio assets of a comprehensive sound library. The models constructed in this work are able to predict the tension, arousal and valence elicited by sound, respectively, with an accuracy of approximately 65%, 66% and 72%.

IEEE Transactions of Affective Computing, vol. 10, no 2, pp. 209-222, 2019. BibTex

Coupling Novelty and Surprise for Evolutionary Divergence

Daniele Gravina, Antonios Liapis and Georgios N. Yannakakis

Abstract: Divergent search techniques applied to evolutionary computation, such as novelty search and surprise search, have demonstrated their efficacy in highly deceptive problems compared to traditional objective-based fitness evolutionary processes. While novelty search rewards unseen solutions, surprise search rewards unexpected solutions. As a result these two algorithms perform a different form of search since an expected solution can be novel while an already seen solution can be surprising. As novelty and surprise search have already shown much promise individually, the hypothesis is that an evolutionary process that rewards both novel and surprising solutions will be able to handle deception in a better fashion and lead to more successful solutions faster. In this paper we introduce an algorithm that realises both novelty and surprise search and we compare it against the two algorithms that compose it in a number of robot navigation tasks. The key findings of this paper suggest that coupling novelty and surprise is advantageous compared to each search approach on its own. The introduced algorithm breaks new ground in divergent search as it outperforms both novelty and surprise in terms of efficiency and robustness, and it explores the behavioural space more extensively.

In Proceedings of the Genetic and Evolutionary Computation Conference, 2017. BibTex

Exploring Divergence in Soft Robot Evolution

Daniele Gravina, Antonios Liapis and Georgios N. Yannakakis

Abstract: Divergent search is a recent trend in evolutionary computation that does not reward proximity to the objective of the problem it tries to solve. Traditional evolutionary algorithms tend to converge to a single good solution, using a fitness proportional to the quality of the problem's solution, while divergent algorithms aim to counter convergence by avoiding selection pressure towards the ultimate objective. This paper explores how a recent divergent algorithm, surprise search, can affect the evolution of soft robot morphologies, comparing the performance and the structure of the evolved robots.

In Proceedings of the Genetic and Evolutionary Computation Conference, 2017. BibTex

Multi-segment Evolution of Dungeon Game Levels

Antonios Liapis

Abstract: This paper presents a generative technique for game levels, focusing on expansive dungeon levels. The proposed two-step evolutionary process creates a high-level overview of the map, which is then used to specify constraints and objectives on multiple constrained optimization algorithms which generate the high-resolution segments of the map. Results show how different types of segments are possible, and how the different connectivity constraints and objectives affect the performance of the algorithm. The modular approach, which allows for a high-level specification of the level first and the subsequent compartmentalized generation of the final map's components, is both scalable and more computationally efficient than a direct encoding, while it allows for more control and user intervention on either level of detail.

In Proceedings of the Genetic and Evolutionary Computation Conference, 2017. BibTex

Board Game Prototyping to Co-Design a Better Location-Based Digital Game

Catherine Emma Jones, Antonios Liapis, Ioanna Lykourentzou and Daniele Guido

Abstract: In this case study we describe the iterative process of paper prototyping, using a board game, to co-design a location-based mobile application. The end goal of the application is to motivate reflection on historical topics about migration. The board game serves to capture the core concerns of this application by simulating movement through the city. Three play tests highlighted the users' interest and issues with the historical content, the way this content is represented, and the players' responses to the interactions and motivating mechanisms of the application. Results show that the board game helped capture important design preferences and problems, ensuring the improvement of our scenario. This feedback can help reduce development effort and implement a future technology prototype closer to the needs of our end users.

In Proceedings of the CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2017. BibTex

Mixed-initiative Creative Drawing with webIconoscope

Antonios Liapis

Abstract: This paper presents the webIcononscope tool for creative drawing, which allows users to draw simple icons composed of basic shapes and colors in order to represent abstract semantic concepts. The goal of this creative exercise is to create icons that are ambiguous enough to confuse other people attempting to guess which concept they represent. webIcononscope is available online and all creations can be browsed, rated and voted on by anyone; this democratizes the creative process and increases the motivation for creating both appealing and ambiguous icons. To complement the creativity of the human users attempting to create novel icons, several computational assistants provide suggestions which alter what the user is currently drawing based on certain criteria such as typicality and novelty. This paper reports trends in the creations of webIcononscope users, based also on feedback from an online audience.

In Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMusArt), vol. 10198, LNCS. Springer, 2017. BibTex

Constructive Generation Methods for Dungeons and Levels

Noor Shaker, Antonios Liapis, Julian Togelius, Ricardo Lopes and Rafael Bidarra

Abstract: This chapter addresses a specific type of game content, the dungeon, and a number of commonly used methods for generating such content. These methods are all "constructive", meaning that they run in fixed (usually short) time, and do not evaluate their output in order to re-generate it. Most of these methods are also relatively simple to implement. And while dungeons, or dungeon-like environments, occur in a very large number of games, these methods can often be made to work for other types of content as well. We finish the chapter by talking about some constructive generation methods for Super Mario Bros. levels.

In Procedural Content Generation in Games: A Textbook and an Overview of Current Research, Springer, 2016. BibTex

Mixed-initiative Content Creation

Antonios Liapis, Gillian Smith and Noor Shaker

Abstract: Algorithms can generate game content, but so can humans. And while PCG algorithms can generate some kinds of game content remarkably well and extremely quickly, some other types (and aspects) of game content are still best made by humans. Can we combine the advantages of procedural generation and human creation somehow? This chapter discusses mixed-initiative systems for PCG, where both humans and software have agency and co-create content. A small taxonomy is presented of different ways in which humans and algorithms can collaborate, and then three mixed-initiative PCG systems are discussed in some detail: Tanagra, Sentient Sketchbook, and Ropossum.

In Procedural Content Generation in Games: A Textbook and an Overview of Current Research, Springer, 2016. BibTex

Can Computers Foster Human Users' Creativity? Theory and Praxis of Mixed-Initiative Co-Creativity

Antonios Liapis, Georgios N. Yannakakis, Constantine Alexopoulos and Phil Lopes

Abstract: This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans' routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.

Digital Culture & Education (DCE), 8 (2). 2016. BibTex

Sonancia: a Multi-Faceted Generator for Horror

Phil Lopes, Antonios Liapis and Georgios N. Yannakakis

Abstract: Fear and tension are the primary emotions elicited by the genre of horror, a peculiar characteristic for media whose sole purpose is to entertain. The audience is often lead into tense and fearful situations, meticulously crafted by the authors using a narrative progression and a combination of visual and auditory stimuli. This paper presents a playable demonstration of the Sonancia system, a multi-faceted content generator for 3D horror games, with the capability of generating levels and their corresponding soundscapes. Designers can also guide the level generation process, by defining an intended progression of tension, which the level generator and sonification will adhere to.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG). 2016. BibTex

A Holistic Approach for Semantic-Based Game Generation

Owen Sacco, Antonios Liapis and Georgios N. Yannakakis

Abstract: The Web contains vast sources of content that could be reused to reduce the development time and effort to create games. However, most Web content is unstructured and lacks meaning for machines to be able to process and infer new knowledge. The Web of Data is a term used to describe a trend for publishing and interlinking previously disconnected datasets on the Web in order to make them more valuable and useful as a whole. In this paper, we describe an innovative approach that exploits Semantic Web technologies to automatically generate games by reusing Web content. Existing work on automatic game content generation through algorithmic means focuses primarily on a set of parameters within constrained game design spaces such as terrains or game levels, but does not harness the potential of already existing content on the Web for game generation. We instead propose a holistic and more generally-applicable game generation solution that would identify suitable Web information sources and enrich game content with semantic meta-structures.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG). 2016. BibTex

Constrained Surprise Search for Content Generation

Daniele Gravina, Antonios Liapis and Georgios N. Yannakakis

Abstract: In procedural content generation, it is often desirable to create artifacts which not only fulfill certain playability constraints but are also able to surprise the player with unexpected potential uses. This paper applies a divergent evolutionary search method based on surprise to the constrained problem of generating balanced and efficient sets of weapons for the Unreal Tournament III shooter game. The proposed constrained surprise search algorithm ensures that pairs of weapons are sufficiently balanced and effective while also rewarding unexpected uses of these weapons during game simulations with artificial agents. Results in the paper demonstrate that searching for surprise can create functionally diverse weapons which require new gameplay patterns of weapon use in the game.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG). 2016. BibTex

Evolving Missions to Create Game Spaces

Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission's starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG). 2016. BibTex

Boosting Computational Creativity with Human Interaction in Mixed-Initiative Co-Creation Tasks

Antonios Liapis and Georgios N. Yannakakis

Abstract: Research in computational creativity often focuses on autonomously creative systems, which incorporate creative processes and result in creative outcomes. However, the integration of artificially intelligent processes in human-computer interaction tools necessitates that we identify how computational creativity can be shaped and ultimately enhanced by human intervention. This paper attempts to connect mixed-initiative design with established theories of computational creativity, and adapt the latter to accommodate a human initiative impacting computationally creative processes and outcomes. Several case studies of mixed-initiative tools for design and play are used to corroborate the arguments in this paper.

in Proceedings of the ICCC workshop on Computational Creativity and Games. 2016. BibTex

Searching for Surprise

Georgios N. Yannakakis and Antonios Liapis

Abstract: Inspired by the notion of surprise for unconventional discovery in computational creativity, we introduce a general search algorithm we name surprise search. Surprise search is grounded in the divergent search paradigm and is fabricated within the principles of metaheuristic (evolutionary) search. The algorithm mimics the self-surprise cognitive process of creativity and equips computational creators with the ability to search for outcomes that deviate from the algorithm's expected behavior. The predictive model of expected outcomes is based on historical trails of where the search has been and some local information about the search space. We showcase the basic steps of the algorithm via a problem solving (maze navigation) and a generative art task. What distinguishes surprise search from other forms of divergent search, such as the search for novelty, is its ability to diverge not from earlier and seen outcomes but rather from predicted and unseen points in the creative domain considered.

in Proceedings of the International Conference on Computational Creativity. 2016. BibTex

Framing Tension for Game Generation

Phil Lopes, Antonios Liapis and Georgios N. Yannakakis

Abstract: Emotional progression in narratives is carefully structured by human authors to create unexpected and exciting situations, often culminating in a climactic moment. This paper explores how an autonomous computational designer can create frames of tension which guide the procedural creation of levels and their soundscapes in a digital horror game. Using narrative concepts, the autonomous designer can describe an intended experience that the automated level generator must adhere to. The level generator interprets this intent, bound by the possibilities and constraints of the game. The tension of the generated level guides the allocation of sounds in the level, using a crowdsourced model of tension.

in Proceedings of the International Conference on Computational Creativity. 2016. BibTex

Murder Mystery Generation from Open Data

Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper describes a system for generating murder mysteries for adventure games, using associations between real-world people mined from Wikipedia articles. A game is seeded with a real-world person, and the game discovers suitable suspects for the murder of a game character instantiated from that person. Moreover, the game discovers characteristics of the suspects which can act as clues for the player to narrow down her search for the killer. The possible suspects and their characteristics are collected from Wikipedia articles and their linked data, while the best combination of suspects and characteristics for a murder mystery is found via evolutionary search. The paper includes an example murder mystery generated by the system revolving around the (hypothetical) death of a contemporary celebrity.

in Proceedings of the International Conference on Computational Creativity. 2016. BibTex

Discovering Social and Aesthetic Categories of Avatars: A Bottom-Up Artificial Intelligence Approach Using Image Clustering

Chong-U Lim, Antonios Liapis and D. Fox Harrell

Abstract: Videogame avatars are more than visual artifacts - they express cultural norms and expectations from both the real world and the fictional world. In this paper, we describe how artificial intelligence clustering can automatically discover distinct characteristics of players' avatars without prior knowledge of a system's underlying data structures. Using only avatar images collected from a study with 191 players, we applied two clustering techniques - namely non-negative matrix factorization and archetypal analysis - that automatically revealed and detected (1) an avatar's gender, (2) regions that appeared to isolate shapes of items and accessories, and (3) aesthetic preferences for particular colors (e.g., bright or muted) and shapes for different body parts. These clusters correlated with players' preferences for character abilities, e.g., male avatars in dark clothes correlated with having high physical but low magic-casting attributes. These findings show that a bottom-up analysis of images can reveal explicit categories like gender, but also implicit categories like preferences of players. We believe that such computational approaches can enable developers to (1) better understand players' desires and needs, (2) quantitatively view how systems may be limited in supporting players, and (3) find actionable solutions for these limitations.

in Proceedings of the International Joint Conference of DiGRA and FDG. 2016. BibTex

Playing with Data: Procedural Generation of Adventures from Open Data

Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper investigates how to generate simple adventure games using open data. We present a system that creates a plot for the player to follow based on associations between Wikipedia articles which link two given topics (in this case people) together. The Wikipedia articles are transformed into game objects (locations, NPCs and items) via constructive algorithms that also rely on geographical information from OpenStreetMaps and visual content from Wikimedia Commons. The different game objects generated in this fashion are linked together via clues which point to one another, while additional false clues and dead ends are added to increase the exploration value of the final adventure game. This information is presented to the user via a set of game screens and images. Inspired by the "Where in the World is Carmen Sandiego?" adventure game, the end result is a generator of chains of followable clues.

in Proceedings of the International Joint Conference of DiGRA and FDG. 2016. BibTex

Surprise Search: Beyond Objectives and Novelty

Daniele Gravina, Antonios Liapis and Georgios N. Yannakakis

Abstract: Grounded in the divergent search paradigm and inspired by the principle of surprise for unconventional discovery in computational creativity, this paper introduces surprise search as a new method of evolutionary divergent search. Surprise search is tested in two robot navigation tasks and compared against objective-based evolutionary search and novelty search. The key findings of this paper reveal that surprise search is advantageous compared to the other two search processes. It outperforms objective search and it is as efficient as novelty search in both tasks examined. Most importantly, surprise search is, on average, faster and more robust in solving the navigation problem compared to objective and novelty search. Our analysis reveals that surprise search explores the behavioral space more extensively and yields higher population diversity compared to novelty search.

in Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 2016. BibTex

Exploring the Visual Styles of Arcade Game Assets

Antonios Liapis

Abstract: This paper describes a method for evolving assets for video games based on their visuals properties. Focusing on assets for a space shooter game, a genotype consisting of turtle commands is transformed into a spaceship image composed of human-authored sprite components. Due to constraints on the final spaceships' plausibility, the paper investigates two-population constrained optimization and constrained novelty search methods. A sample of visual styles is tested, each a combination of visual metrics which primarily evaluate balance and shape complexity. Experiments with constrained optimization of a visual style demonstrate that a visually consistent set of spaceships can be generated, while experiments with constrained novelty search demonstrate that several distinct visual styles can be discovered by exploring along select, or all, visual dimensions.

in Proceedings of Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMusArt). Springer, 2016. BibTex

Monte-Carlo Tree Search for Persona Based Player Modeling

Christoffer Holmgard, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: Is it possible to conduct player modeling without any players? In this paper we use Monte-Carlo Tree Search-controlled procedural personas to simulate a range of decision making styles in the puzzle game MiniDungeons 2. The purpose is to provide a method for synthetic play testing of game levels with synthetic players based on designer intuition and experience. Five personas are constructed, representing five different decision making styles archetypal for the game. The personas vary solely in the weights of decision-making utilities that describe their valuation of a set affordances in MiniDungeons 2. By configuring these weights using designer expert knowledge, and passing the configurations directly to the MCTS algorithm, we make the personas exhibit a number of distinct decision making and play styles.

in Proceedings of the AIIDE workshop on Player Modeling, 2015. BibTex

Map Sketch Generation as a Service

Antonios Liapis

Abstract: This paper describes the structure of a webservice able to generate simple game levels via constrained evolutionary optimization. The provided webservice allows users to generate playable game levels without needing to understand the underlying process and without having to allocate computational resources for doing so; combined with the highly expressive and customizable generator, a broad range of levels for different genres and purposes can meet many user needs.

in Proceedings of the AIIDE workshop on Experimental AI in Games, 2015. BibTex

Targeting Horror via Level and Soundscape Generation

Phil Lopes, Antonios Liapis and Georgios N Yannakakis

Abstract: Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player's tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them.

in Proceedings of the AAAI Artificial Intelligence for Interactive Digital Entertainment Conference, 2015. BibTex

Refining the Paradigm of Sketching in AI-Based Level Design

Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper describes computational processes which can simulate how human designers sketch and then iteratively refine their work. The paper uses the concept of a map sketch as an initial, low-resolution and low-fidelity prototype of a game level, and suggests how such map sketches can be refined computationally. Different case studies with map sketches of different genres showcase how refinement can be achieved via increasing the resolution of the game level, increasing the fidelity of the function which evaluates it, or a combination of the two. While these case studies use genetic algorithms to automatically generate levels at different degrees of refinement, the general method described in this paper can be used with most procedural generation methods, as well as for AI-assisted design alongside a human creator.

in Proceedings of the AAAI Artificial Intelligence for Interactive Digital Entertainment Conference, 2015. BibTex

Multi-Level Evolution of Shooter Levels

William Cachia, Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper introduces a search-based generative process for first person shooter levels. Genetic algorithms evolve the level's architecture and the placement of powerups and player spawnpoints, generating levels with one floor or two floors. The evaluation of generated levels combines metrics collected from simulations of artificial agents competing in the level and theory-based heuristics targeting general level design patterns. Both simulation-based and theory-driven evaluations target player balance and exploration, while resulting levels emergently exhibit several popular design patters of shooter levels.

in Proceedings of the AAAI Artificial Intelligence for Interactive Digital Entertainment Conference, 2015. BibTex

Sonancia: Sonification of Procedurally Generated Game Levels

Phil Lopes, Antonios Liapis and Georgios N Yannakakis

Abstract: How can creative elements brought from level design effectively be coupled with audio in order to create tense and engaging player experiences? In this paper the above question is posed through the sonification of procedurally generated digital game levels. The paper details some initial approaches and methodologies for achieving this core aim.

in Proceedings of the ICCC workshop on Computational Creativity & Games, 2015. BibTex

A Constructive Approach for the Generation of Underwater Environments

Ryan Abela, Antonios Liapis and Georgios N. Yannakakis

Abstract: This paper introduces Coralize, a library of generators for marine organisms such as corals and sponges. Using constructive algorithms, Coralize can generate stony corals via L-system grammars, soft corals via leaf venation algorithms and sponges via nutrient-based mesh growth. The generative algorithms are parameterizable, allowing a user to adjust the parameters in order to create visually appealing 3D meshes. Such meshes can be used to automatically populate a seabed or reef, in order to create a biologically realistic and aesthetically pleasing underwater environment.

in Proceedings of the FDG workshop on Procedural Content Generation in Games, 2015. BibTex

Data Adventures

Gabriella A. B. Barros, Antonios Liapis and Julian Togelius

Abstract: This paper outlines a system for generating adventure games based on open data, and describes a sketch of the system implementation at its current state. The adventure game genre has been popular for a long time and differs significantly in design priorities from game genres which are commonly addressed in PCG research. In order to create believable and engaging content, we use data from DBpedia to generate the game's non-playable characters locations and plot, and OpenStreetMaps to create the game's levels.

in Proceedings of the FDG workshop on Procedural Content Generation in Games, 2015. BibTex

Motivating Visual Interpretations in Iconoscope: Designing a Game for Fostering Creativity

Antonios Liapis, Amy K. Hoover, Georgios N. Yannakakis, Constantine Alexopoulos and Evangelia V. Dimaraki

Abstract: This paper introduces Iconoscope, a game aiming to foster the creativity of a young target audience in formal or informal educational settings. At the core of the Iconoscope design is the creative, playful interpretation of word-concepts via the construction of visual icons. In addition to that, then game rewards ambiguity via a scoring system which favors icons that dichotomize public opinion. The game is played by a group of players, with each player attempting to guess which of the concepts provided by the system is represented by each opponent's created icon. Through the social interaction that emerges, Iconoscope prompts co-creativity within a group of players; in addition, the game offers the potential of human-machine co-creativity via computer-generated suggestions to the player's icon. Experiments with early prototypes, described in this paper, provide insight into the design process and motivate certain decisions taken for the current version of Iconoscope which, at the time of writing, is being evaluated in selected schools in Greece, Austria and the United Kingdom.

in Proceedings of the 10th Conference on the Foundations of Digital Games, 2015. BibTex

MiniDungeons 2: An Experimental Game for Capturing and Modeling Player Decisions

Christoffer Holmgard, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: This paper describes MiniDungeons 2 (MD2): a turn-based rogue-like game developed to support research in capturing and modeling player decision making processes through procedural personas and using such models as critics for procedural content generation. MD2 intends to provide a full-circle framework for collecting, modeling, simulating, and producing content for player decision making styles. The fully instrumented and telemetric game will soon be made available to the public to be played on smart-phones for the purpose of collecting as many play traces, representing as many different decision making styles, as possible.

in Proceedings of the 10th Conference on the Foundations of Digital Games. 2015. BibTex

Evolving Models of Player Decision Making: Personas versus Clones

Christoffer Holmgard, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: The current paper investigates multiple approaches to modeling human decision making styles for procedural play-testing. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Three kinds of agents are evolved from the same representation: procedural personas, evolved from game designer expert knowledge, clones, evolved from observations of human play and aimed at general behavioral replication, and specialized agents, also evolved from observation, but aimed at determining the maximal behavioral replication ability of the representation. These three methods are then compared on their ability to represent individual human decision makers. Comparisons are conducted using three different proposed metrics that address the problem of matching decisions at the action, tactical, and strategic levels. Results indicate that a small gallery of personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play-traces for the testbed game MiniDungeons.

Entertainment Computing. Elsevier Volume 16, 2016, pp. 95–104. BibTex

Procedural Personas as Critics for Dungeon Generation

Antonios Liapis, Christoffer Holmgard, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper introduces a constrained optimization method which uses procedural personas to evaluate the playability and quality of evolved dungeon levels. Procedural personas represent archetypical player behaviors, and their controllers have been evolved to maximize a specific utility which drives their decisions. A "baseline" persona evaluates whether a level is playable by testing if it can survive in a worst-case scenario of the playthrough. On the other hand, a Monster Killer persona or a Treasure Collector persona evaluates playable levels based on how many monsters it can kill or how many treasures it can collect, respectively. Results show that the implemented two-population genetic algorithm discovers playable levels quickly and reliably, while the different personas affect the layout, difficulty level and tactical depth of the generated dungeons.

in Applications of Evolutionary Computation, vol. 9028, LNCS. Springer, 2015. BibTex

DrawCompileEvolve: Sparking Interactive Evolutionary Art with Human Creations

Jinhong Zhang, Rasmus Taarnby, Antonios Liapis and Sebastian Risi

Abstract: This paper presents DrawCompileEvolve, a web-based drawing tool which allows users to draw simple primitive shapes, group them together or define patterns in their groupings (e.g. symmetry, repetition). The user's vector drawing is then compiled into an indirectly encoded genetic representation, which can then be evolved interactively, allowing the user to change the image's colors, patterns and ultimately transform it. The human artist has direct control while drawing the initial seed of an evolutionary run and indirect control while interactively evolving it, thus making DrawCompileEvolve a mixed-initiative art tool. Early results in this paper show the potential of DrawCompileEvolve to jump-start evolutionary art with meaningful drawings as well as the power of the underlying genetic representation to transform the user's initial drawing into a different, yet potentially meaningful, artistic rendering.

in Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMusArt), vol. 9027, LNCS. Springer, 2015. BibTex

AudioInSpace: Exploring the Creative Fusion of Generative Audio Visuals and Gameplay

Amy K. Hoover, William Cachia, Antonios Liapis and Georgios N. Yannakakis

Abstract: Computer games are unique creativity domains in that they elegantly fuse several facets of creative work including visuals, narrative, music, architecture and design. While the exploration of possibilities across facets of creativity offers a more realistic approach to the game design process, most existing autonomous (or semi-autonomous) game content generators focus on the mere generation of single domains (creativity facets) in games. Motivated by the sparse literature on multifaceted game content generation, this paper introduces a multifaceted procedural content generation (PCG) approach that is based on the interactive evolution of multiple artificial neural networks that orchestrate the generation of visuals, audio and gameplay. The approach is evaluated on a spaceship shooter game. The generated artifacts - a fusion of audiovisual and gameplay elements - showcase the capacity of multifaceted PCG and its evident potential for computational game creativity.

in Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMusArt), vol. 9027, LNCS. Springer, 2015. BibTex

Personas versus Clones for Player Decision Modeling

Christoffer Holmgard, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: The current paper investigates how to model human play styles. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Two methods are developed, applied, and compared: procedural personas, based on utilities designed with expert knowledge, and clones, trained to reproduce playtraces. Additionally, two metrics for comparing agent and human decision making styles are proposed and compared. Results indicate that personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human playtraces.

in Proceedings of the International Conference on Entertainment Computing (ICEC), 2014. BibTex

Searching for Sentient Design Tools for Game Development

Antonios Liapis

Abstract: Over the last twenty years, computer games have grown from a niche market targeting young adults to an important player in the global economy, engaging millions of people from different cultural backgrounds. As both the number and the size of computer games continue to rise, game companies handle increasing demand by expanding their cadre, compressing development cycles and reusing code or assets. To limit development time and reduce the cost of content creation, commercial game engines and procedural content generation are popular shortcuts. Content creation tools are means to either generate a large volume of game content or to reduce designer effort by automating the mechanizable aspects of content creation, such as feasibility checking. However elaborate the type of content such tools can create, they remain subservient to their human developers/creators (who have tightly designed all their generative algorithms) and to their human users (who must take all design decisions), respectively. This thesis argues that computers can be creative partners to human designers rather than mere slaves; game design tools can be aware of designer intentions, preferences and routines, and can accommodate them or even subvert them. This thesis presents Sentient Sketchbook, a tool for designing game level abstractions of different game genres, which assists the level designer as it automatically tests maps for playability constraints, evaluates and displays the map's gameplay properties and creates alternatives to the user's current design in order to speed up the creation process and inspire the user to think outside the box. Several AI techniques are implemented, and others invented, for the purposes of creating meaningful suggestions for Sentient Sketchbook as well as for adapting these suggestions to the user's own preferences. While the thesis focuses on the design, performance, and human use of Sentient Sketchbook, the same algorithms and concepts can be applied to different mixed-initiative tools, a subset of which has been implemented and is presented in this thesis.

PhD thesis, Center for Computer Games, IT University of Copenhagen, Copenhagen, Denmark, 2014. BibTex

Designer Modeling for Sentient Sketchbook

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper documents the challenges in creating a computer-aided level design tool which incorporates computer-generated suggestions which appeal to the human user. Several steps are suggested in order to make the suggestions more appropriate to a specific user's overall style, current focus, and end-goals. Designer style is modeled via choice-based interactive evolution which adapts the impact of different dimensions of quality based on the designer's choice of certain suggestions over others. Modeling process is carried out similarly to style, but adapting to the current focus of the designer's actions. Goals are modeled by estimating the visual patterns of the designer's final artifact and changing the parameters of the algorithm to enforce such patterns on generated suggestions.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG), 2014. BibTex

Searching for Good and Diverse Game Levels

Mike Preuss, Antonios Liapis and Julian Togelius

Abstract: In procedural content generation, one is often interested in generating a large number of artifacts that are not only of high quality but also diverse, in terms of gameplay, visual impression or some other criterion. We investigate several search-based approaches to creating good and diverse game content, in particular approaches based on evolution strategies with or without diversity preservation mechanisms, novelty search and random search. The content domain is game levels, more precisely map sketches for strategy games, which are meant to be used as suggestions in the Sentient Sketchbook design tool. Several diversity metrics are possible for this type of content: we investigate tile-based, objective-based and visual impression distance. We find that evolution with diversity preservation mechanisms can produce both good and diverse content, but only when using appropriate distance measures. Reversely, we can draw conclusions about the suitability of these distance measures for the domain from the comparison of diversity preserving versus blind restart evolutionary algorithms.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG), 2014. BibTex

Evolving Personas for Player Decision Modeling

Christoffer Holmgard, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: This paper explores how evolved game playing agents can be used to represent a priori defined archetypical ways of playing a test-bed game, as procedural personas. The end goal of such procedural personas is substituting players when authoring game content manually, procedurally, or both (in a mixed-initiative setting). Building on previous work, we compare the performance of newly evolved agents to agents trained via Q-learning as well as a number of baseline agents. Comparisons are performed on the grounds of game playing ability, generalizability, and conformity among agents. Finally, all agents' decision making styles are matched to the decision making styles of human players in order to investigate whether the different methods can yield agents who mimic or differ from human decision making in similar ways. The experiments performed in this paper conclude that agents developed from a priori defined objectives can express human decision making styles and that they are more generalizable and versatile than Q-learning and hand-crafted agents.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG), 2014. BibTex

The Case for a Mixed-Initiative Collaborative Neuroevolution Approach

Sebastian Risi, Jinhong Zhang, Rasmus Taarnby, Peter Greve, Jan Piskur, Antonios Liapis and Julian Togelius

Abstract: It is clear that the current attempts at using algorithms to create artificial neural networks have had mixed success at best when it comes to creating large networks and/or complex behavior. This should not be unexpected, as creating an artificial brain is essentially a design problem. Human design ingenuity still surpasses computational design for most tasks in most domains, including architecture, game design, and authoring literary fiction. This leads us to ask which the best way is to combine human and machine design capacities when it comes to designing artificial brains. Both of them have their strengths and weaknesses; for example, humans are much too slow to manually specify thousands of neurons, let alone the billions of neurons that go into a human brain, but on the other hand they can rely on a vast repository of common-sense understanding and design heuristics that can help them perform a much better guided search in design space than an algorithm. Therefore, in this paper we argue for a mixed-initiative approach for collaborative online brain building and present first results towards this goal.

in Proceedings of the ALIFE workshop on Artificial Life and the Web, 2014. BibTex

Computational Game Creativity

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: Computational creativity has traditionally relied on well-controlled, single-faceted and established domains such as visual art, narrative and audio. On the other hand, research on autonomous generation methods for game artifacts has not yet considered the creative capacity of those methods. In this paper we position computer games as the ideal application domain for computational creativity for the unique features they offer: being highly interactive, dynamic and content-intensive software applications. Their multifaceted nature is key in our argumentation as the successful orchestration of different art domains (such as visual art, audio and level architecture) with game mechanics design is a grand challenge for the study of computational creativity in this multidisciplinary domain. Computer games not only challenge computational creativity and provide a creative sandbox for advancing the field but they also offer an opportunity for computational creativity methods to be extensively assessed (via a huge population of gamers) through commercial-standard products of high impact and financial value.

in Proceedings of the Fifth International Conference on Computational Creativity, 2014. BibTex

Constrained Novelty Search: A Study on Game Content Generation

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: Novelty search is a recent algorithm geared towards exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.

Evolutionary Computation 21(1), 2015, pp. 101-129. BibTex

Mixed-Initiative Co-Creativity

Georgios N. Yannakakis, Antonios Liapis and Constantine Alexopoulos

Abstract: Creating and designing with a machine: do we merely create together (co-create) or can a machine truly foster our creativity as human creators? When does such co-creation foster the co-creativity of both humans and machines? This paper investigates the simultaneous and/or iterative process of human and computational creators in a mixed-initiative fashion within the context of game design and attempts to draw from both theory and praxis towards answering the above questions. For this purpose, we first discuss the strong links between mixed-initiative co-creation and theories of human and computational creativity. We then introduce an assessment methodology of mixed-initiative co-creativity and, as a proof of concept, evaluate Sentient Sketchbook as a co-creation tool for game design. Core findings suggest that tools such as Sentient Sketchbook are not mere game authoring systems or mere enablers of creation but, instead, foster human creativity and realize mixed-initiative co-creativity.

in Proceedings of the 9th Conference on the Foundations of Digital Games, 2014. BibTex

Generative Agents for Player Decision Modeling in Games

Christoffer Holmgard, Antonios Liapis, Julian Togelius and Georgios N. Yannakakis

Abstract: This paper presents a method for modeling player decision making through the use of agents as AI-driven personas. The paper argues that artificial agents, as generative player models, have properties that allow them to be used as psychometrically valid, abstract simulations of a human player's internal decision making processes. Such agents can then be used to interpret human decision making, as personas and playtesting tools in the game design process, as baselines for adapting agents to mimic classes of human players, or as believable, human-like opponents. This argument is explored in a crowdsourced decision making experiment, in which the decisions of human players are recorded in a small-scale dungeon themed puzzle game. Human decisions are compared to the decisions of a number of a priori defined "archetypical" agent-personas, and the humans are characterized by their likeness to or divergence from these. Essentially, at each step the action of the human is compared to what actions a number of reinforcement-learned agents would have taken in the same situation, where each agent is trained using a different reward scheme. Finally, extensions are outlined for adapting the agents to represent sub-classes found in the human decision making traces.

in Poster Proceedings of the 9th Conference on the Foundations of Digital Games, 2014. BibTex

Characteristics of Generatable Games

Julian Togelius, Mark J. Nelson and Antonios Liapis

Abstract: We address the problem of generating complete games, rather than content for existing games. In particular, we try to answer the question which types of games it would be realistic or even feasible to generate. To begin to answer the question, we first list the different ways we see that games could be generated, and then try to discuss what characterises games that would be comparatively easy or hard to generate. The discussion is structured according to a subset of the characteristics discussed in the book Characteristics of Games by Elias, Garfield and Gutschera.

in Proceedings of the FDG Workshop on Procedural Content Generation, 2014. BibTex

Towards a Generic Method of Evaluating Game Levels

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper addresses the problem of evaluating the quality of game levels across different games and even genres, which is of key importance for making procedural content generation and assisted game design tools more generally applicable. Three game design patterns are identified for having high generality while being easily quantifiable: area control, exploration and balance. Formulas for measuring the extent to which a level includes these concepts are proposed, and evaluation functions are derived for levels in two different game genres: multiplayer strategy game maps and single-player roguelike dungeons. To illustrate the impact of these evaluation functions, and the similarity of impact across domains, sets of levels for each function are generated using a constrained genetic algorithm. The proposed measures can easily be extended to other game genres.

in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2013. BibTex

Designer Modeling for Personalized Game Content Creation Tools

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: With the growing use of automated content creation and computer-aided design tools in game development, there is potential for enhancing the design process through personalized interactions between the software and the game developer. This paper proposes designer modeling for capturing the designer's preferences, goals and processes from their interaction with a computer-aided design tool, and suggests methods and domains within game development where such a model can be applied. We describe how designer modeling could be integrated with current work on automated and mixed-initiative content creation, and envision future directions which focus on personalizing the processes to a designer's particular wishes.

in Proceedings of the AIIDE Workshop on Artificial Intelligence & Game Aesthetics, 2013. BibTex

Adaptive Game Level Creation through Rank-based Interactive Evolution

Antonios Liapis, Hector P. Martinez, Julian Togelius and Georgios N. Yannakakis

Abstract: This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG), 2013. BibTex

Enhancements to Constrained Novelty Search: Two-Population Novelty Search for Generating Game Content

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: Novelty search is a recent algorithm geared to explore search spaces without regard to objectives; minimal criteria novelty search is a variant of this algorithm for constrained search spaces. For large search spaces with multiple constraints, however, it is hard to find a set of feasible individuals that is both large and diverse. In this paper, we present two new methods of novelty search for constrained spaces, Feasible-Infeasible Novelty Search and Feasible-Infeasible Dual Novelty Search. Both algorithms keep separate populations of feasible and infeasible individuals, inspired by the FI-2pop genetic algorithm. These algorithms are applied to the problem of creating diverse and feasible game levels, representative of a large class of important problems in procedural content generation for games. Results show that the new algorithms under certain conditions can produce larger and more diverse sets of feasible strategy game maps than existing algorithms. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. It is also shown that the proposed enhancement of offspring boosting increases performance in all cases.

in Proceedings of the Genetic and Evolutionary Competition Conference, 2013, pp. 343-350. BibTex

Transforming Exploratory Creativity with DeLeNoX

Antonios Liapis, Hector P. Martinez, Julian Togelius and Georgios N. Yannakakis

Abstract: We introduce DeLeNoX (Deep Learning Novelty Explorer), a system that autonomously creates artifacts in constrained spaces according to its own evolving interestingness criterion. DeLeNoX proceeds in alternating phases of exploration and transformation. In the exploration phases, a version of novelty search augmented with constraint handling searches for maximally diverse artifacts using a given distance function. In the transformation phases, a deep learning autoencoder learns to compress the variation between the found artifacts into a lower-dimensional space. The newly trained encoder is then used as the basis for a new distance function, transforming the criteria for the next exploration phase. In the current paper, we apply DeLeNoX to the creation of spaceships suitable for use in two-dimensional arcade-style computer games, a representative problem in procedural content generation in games. We also situate DeLeNoX in relation to the distinction between exploratory and transformational creativity, and in relation to Schmidhuber's theory of creativity through the drive for compression progress.

in Proceedings of the Fourth International Conference on Computational Creativity, 2013, pp. 56-63. BibTex

Sentient Sketchbook: Computer-Aided Game Level Authoring

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper introduces the Sentient Sketchbook, a tool which supports a designer in the creation of game levels. Using map sketches to alleviate designer effort, the tool automates playability checks and evaluations and visualizes significant gameplay properties. This paper also introduces constrained novelty search via a two-population paradigm for generating, in real-time, alternatives to the author's design and evaluates its potential against current approaches. The paper concludes with a small-scale user study in which industry experts interact with the Sentient Sketchbook to design game levels. Results demonstrate the tool's potential and provide directions for its improvement.

in Proceedings of the 8th Conference on the Foundations of Digital Games, 2013, pp. 213-220. BibTex

Sentient World: Human-Based Procedural Cartography

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper presents a first step towards a mixed-initiative tool for the creation of game maps. The tool, named Sentient World, allows the designer to draw a rough terrain sketch, adding extra levels of detail through stochastic and gradient search. Novelty search generates a number of dissimilar artificial neural networks that are trained to approximate a designer's sketch and provide maps of higher resolution back to the designer. As the procedurally generated maps are presented to the designer (to accept, reject, or edit) the terrain sketches are iteratively refined into complete high resolution maps which may diverge from initial designer concepts. The tool supports designer creativity while conforming to designer intentions, and maintains constant designer control through the map selection and map editing options. Results obtained on a number of test maps show that novelty search is beneficial for introducing divergent content to the designer without reducing the speed of iterative map refinement.

in Proceedings of Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMusArt), vol. 7834, LNCS. Springer, 2013, pp. 180-191. BibTex

Generating Map Sketches for Strategy Games

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: How can a human and an algorithm productively collaborate on generating game content? In this paper, we try to answer this question in the context of generating balanced and interesting low-resolution sketches for game levels. We introduce six important criteria for successful strategy game maps, and present map sketches optimized for one or more of these criteria via a constrained evolutionary algorithm. The sketch-based map representation and the computationally lightweight evaluation methods are geared towards the integration of the evolutionary algorithm within a mixed-initiative tool, allowing for the co-creation of game content by a human and an artificial designer.

in Proceedings of Applications of Evolutionary Computation, vol. 7835, LNCS. Springer, 2013, pp. 264-273. BibTex

Limitations of Choice-Based Interactive Evolution for Game Level Design

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper presents a tool geared towards the collaboration of a human and an artificial designer for the creation of game content. The framework combines procedural content generation using stochastic search with user input in the form of an initial goal statement as well as preference of generated results. Feedback from industry experts in a pilot user experiment showcased the limitations of this approach and the protocol chosen for evaluating the authoring tool. The limitations are discussed with respect to the suitability of interactive evolution for creative design and the design of experimental protocols for evaluating authoring tools for games.

in Proceedings of the AIIDE Workshop on Human Computation in Digital Entertainment, 2012. BibTex

Adapting Models of Visual Aesthetics for Personalized Content Creation

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper introduces a search-based approach to personalized content generation with respect to visual aesthetics. The approach is based on a two-step adaptation procedure where (1) the evaluation function that characterizes the content is adjusted to match the visual aesthetics of users and (2) the content itself is optimized based on the personalized evaluation function. To test the efficacy of the approach we design fitness functions based on universal properties of visual perception, inspired by psychological and neurobiological research. Using these visual properties we generate aesthetically pleasing 2D game spaceships via neuroevolutionary constrained optimization and evaluate the impact of the designed visual properties on the generated spaceships. The offline generated spaceships are used as the initial population of an interactive evolution experiment in which players are asked to choose spaceships according to their visual taste: the impact of the various visual properties is adjusted based on player preferences and new content is generated online based on the updated computational model of visual aesthetics of the player. Results are presented which show the potential of the approach in generating content which is based on subjective criteria of visual aesthetics.

IEEE Transactions on Computational Intelligence and AI in Games 4(3), 2012, pp. 213-228. BibTex

Optimizing Visual Properties of Game Content through Neuroevolution

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper presents a search-based approach to generating game content that satisfies both gameplay requirements and user-expressed aesthetic criteria. Using evolutionary constraint satisfaction, we search for spaceships (for a space combat game) represented as compositional pattern-producing networks. While the gameplay requirements are satisfied by ad-hoc defined constraints, the aesthetic evaluation function can also be informed by human aesthetic judgement. This is achieved using indirect interactive evolution, where an evaluation function re-weights an array of aesthetic criteria based on the choices of a human player. Early results show that we can create aesthetically diverse and interesting spaceships while retaining in-game functionality.

in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2011. BibTex

Neuroevolutionary Constrained Optimization for Content Creation

Antonios Liapis, Georgios N. Yannakakis and Julian Togelius

Abstract: This paper presents a constraint-based procedural content generation (PCG) framework used for the creation of novel and high-performing content. Specifically, we examine the efficiency of the framework for the creation of spaceship design (hull shape and spaceship attributes such as weapon and thruster types and topologies) independently of game physics and steering strategies. According to the proposed framework, the designer picks a set of requirements for the spaceship that a constrained optimizer attempts to satisfy. The constraint satisfaction approach followed is based on neuroevolution; Compositional Pattern-Producing Networks (CPPNs) which represent the spaceship's design are trained via a constrain-based evolutionary algorithm. Results obtained in a number of evolutionary runs using a set of constraints and objectives show that the generated spaceships perform well in movement, combat and survival tasks and are also visually appealing.

in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG), 2011, pp. 71-78. BibTex