Neural/Deep Networks

Whether to construct predictive models of user behavior or player affect, to find mappings between game elements and their aesthetics, or to figure out the goals of a human co-creator, machine learning based on neural networks is a powerful tool for realizing artificial intelligence.

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

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

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

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

Designer Modeling for Sentient Sketchbook

Antonios Liapis, Georgios N. Yannakakis, 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

Adaptive Game Level Creation through Rank-based Interactive Evolution

Antonios Liapis, Hector P. Martinez, Julian Togelius, 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

Transforming Exploratory Creativity with DeLeNoX

Antonios Liapis, Hector P. Martinez, Julian Togelius, 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 World: Human-Based Procedural Cartography

Antonios Liapis, Georgios N. Yannakakis, 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

Limitations of Choice-Based Interactive Evolution for Game Level Design

Antonios Liapis, Georgios N. Yannakakis, 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, 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, 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