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Dagstuhl-Seminar 15051

Artificial and Computational Intelligence in Games: Integration

( 25. Jan – 30. Jan, 2015 )


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Motivation

This seminar will bring together researchers and industry representatives who work at the forefront of artificial intelligence (AI) and computational intelligence (CI) in games. We will assemble practitioners and researchers working on different topics within the broader game AI field with the explicit purpose of investigating how their ideas, algorithms and domains can be combined. The seminar will include experts on topics from game tree search to machine learning to player modeling to content generation, in a wide range of games: from board games to commercial video games and serious games.

The video game industry is the largest of the entertainment industries and is growing rapidly. While modern games offer stunning graphics, the quality of the AI has for a long time been a weak point of many games. There is now a great deal of interest both from the game industry and from academic AI researchers in improving game AI, and in novel applications of AI methods such as procedural content generation, player experience optimization, and automated testing. However, the field is divided, with researchers and practitioners in different subfields not being aware of each others' work. There are a number of recently or not-so-recently developed AI solutions which promise to improve the quality of game AI, but which are not commonly known either in industry or in neighboring academic fields. We expect significant synergies and even new research directions to result from explicitly addressing the integration of these techniques and fields within this Dagstuhl Seminar.

In the previous seminar, groups gathered around a particular problem (e.g. player modeling or content generation) or a particular technique (e.g. search or planning) as applied to games. In this seminar, working groups will explicitly address the intersection of different techniques and problems (e.g.: How can planning be used in content generation? How can search techniques be used for learning human-like behavior?) and the use and modification of these techniques for game design and development (e.g.: How can we build game designs around player modeling? What are the requirements on case-based reasoning for effective use in game engines?). The idea is that leading experts in different techniques and problems assemble in groups in a semi-structured process, and through creative cross-fertilization proceed to solve problems and create new research topics. The main expected outcome of the seminar is a better understanding of how to integrate novel AI into games, game design, and related applications, and how to combine different game AI techniques.


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Summary

The research field of artificial and computational intelligence in games focuses on the wide variety of advanced computational techniques used to create, enhance, and improve the experiences of humans interacting with and within virtual environments. By its nature the field is broad and multidisciplinary. People working in it include academic researchers from a variety of disciplines, corporate researchers in the games industry as well as in other industries, and independent game developers. The methods used include symbolic AI techniques such as reasoning, constraint-solving and partial-order planning as well as biologically-inspired techniques such as evolutionary computation and neural networks, statistical techniques such as support vector machines and clustering, as well as special-purpose techniques such as behavior trees. These are applied to games ranging from board and card games to first-person shooters and real-time strategy games as well as abstract mathematical games, and are used to, for instance, play games, model players, tell stories, generate levels and other game content, and match players. Different researchers have different goals, including developing and testing new AI methods, creating interesting and believable non-player characters, improving the game production pipeline, studying game design through computational means, and understanding players and patterns of interaction. Often several goals overlap in the same project.

Recent years have seen considerable progress in several of the techniques used in the field, as well as rapid development in what kind of research questions are asked and what kind of games are studied with which methods. It has become increasingly clear that many of the research goals require a multidisciplinary approach, or at least a combination of techniques that, in the past, were considered separate research topics. For instance, with respect to the behavior of virtual agents, ten years ago researchers mainly aimed at making such behavior more "effective," which can often be achieved with straightforward computational reasoning. Nowadays, however, researchers aim at making the behavior of virtual agents more "natural" in their interaction with humans, which requires contributions not only from computer science, but also from psychology and social sciences, and which requires a wide variety of techniques, such as player modeling, adaptation, reasoning, and computational linguistics.

To move the research field forward, it is therefore of crucial importance to facilitate the integration of the disciplines and techniques that are involved in this research. The various strands, methodological approaches, and research directions need to inform each other and collaborate, to achieve a whole that is more than the sum of its parts. The goal of the second Dagstuhl Seminar on Computational and Artificial Intelligence in Games was to explicitly take the first steps along this path of integration, and investigate which topics and techniques would benefit most from collaboration, how collaboration could be shaped, and which new research questions may potentially be answered.

The seminar was held between January 25 and January 30, 2015. To stimulate interaction between the participants, which is essential in this context, the seminar was structured around workgroups rather than presentations. The organizers started the seminar on Monday morning with a series of brief presentations on potential discussion topics, after which the participants formed their own workgroups around a variety of topics, not necessarily those brought up by the organizers. Workgroups typically consisted of 3 to 10 people from different backgrounds, who worked together for no more than one day. At regular intervals workgroups reported on their findings in a plenary session, after which new workgroups were formed.

At the start of the seminar it was announced that Thursday would be set aside for practical work. Participants could use that day to implement some of the ideas that had come up in the previous days, in the form of a game, a competition, a design document, or a research proposal. While the organizers deliberately gave the participants the option to simply continue with the workgroups if they so wished, all participants actually got involved in the practical work, some of them even working on multiple projects in parallel.

The results of the workgroups and the practical sessions are briefly related in the remainder of these proceedings. The 13 abstracts on workgroups cover automated and AI-based game design; game analytics; interdisciplinary research methods; design of believable characters; general video game playing; creativity facet orchestration; methods and formal design for procedural content generation; design of "fun" gameplaying bots; communication on game AI research, computers that play like humans; and neural networks for games. The 11 abstracts on practical sessions cover the Planet Wars competition; the automatic generation of games, mazes, and text; Twitter bots; sonification of character reasoning; MCTS and representation learning for procedural content generation; two AI-based games; and the design for a board game.

A special issue of the IEEE Transactions on Computational and Artificial Intelligence in Games will be published on the topic of this Dagstuhl Seminar. While this issue is open for submission for any researcher in this field, it is expected that several of the workgroups of the seminar will submit papers on their results.

As organizers, we knew that the first seminar that we organized in 2012 was considered a great success, and we had expected more people to accept our invitations for this second seminar than for the previous one. However, demand for attending the seminar was even greater than we expected. Almost everyone we first invited immediately accepted our invitation. Moreover, everybody who accepted their invitation indeed showed up at the seminar. We were forced by capacity concerns to not invite many people who, by their strength of contribution in the field, should have been present. We are certain that we could easily have doubled the number of participants visiting the seminar, and that each of those participants would have made a strong contribution.

The value of these Dagstuhl Seminars is indisputable. Considering the large number of researchers that should be invited to a seminar that attempts to cover the whole, very broad research field of Computational and Artificial Intelligence in Games, we believe that it is wise for a future seminar to narrow down the topic, so that it can be restricted to a smaller number of participants that are active in the selected subfield. Naturally, considering the fact that "integration" is such an important issue in the research field, care must be taken to ensure that every discipline interested in and involved in the subfield is represented.

Copyright Simon M. Lucas and Michael Mateas and Mike Preuss and Pieter Spronck and Julian Togelius

Teilnehmer
  • Dan Ashlock (University of Guelph, CA) [dblp]
  • Christian Bauckhage (Fraunhofer IAIS - St. Augustin, DE) [dblp]
  • Rafael Bidarra (TU Delft, NL) [dblp]
  • Bruno Bouzy (Paris Descartes Unversity, FR) [dblp]
  • Michael Buro (University of Alberta - Edmonton, CA) [dblp]
  • Alex J. Champandard (AiGameDev.com KG - Wien, AT) [dblp]
  • Simon Colton (University of London/Goldsmiths, GB) [dblp]
  • Michael Cook (University of London/Goldsmiths, GB) [dblp]
  • Peter I. Cowling (University of York, GB) [dblp]
  • Ian Horswill (Northwestern University - Evanston, US) [dblp]
  • Graham Kendall (University of Nottingham, GB) [dblp]
  • Pier Luca Lanzi (Polytechnic University of Milan, IT) [dblp]
  • John M. Levine (University of Strathclyde, GB) [dblp]
  • Antonios Liapis (IT University of Copenhagen, DK) [dblp]
  • Simon M. Lucas (University of Essex, GB) [dblp]
  • Brian Magerko (Georgia Institute of Technology, US) [dblp]
  • Michael Mateas (University of California - Santa Cruz, US) [dblp]
  • Joshua Allen McCoy (University of California - Santa Cruz, US) [dblp]
  • Mark J. Nelson (IT University of Copenhagen, DK) [dblp]
  • Santiago Ontañón (Drexel Univ. - Philadelphia, US) [dblp]
  • Ana Paiva (INESC-ID - Porto Salvo, PT) [dblp]
  • Mirjam Palosaari Eladhari (University of Malta, MT) [dblp]
  • Mike Preuß (Universität Münster, DE) [dblp]
  • Günter Rudolph (TU Dortmund, DE) [dblp]
  • Spyridon Samothrakis (University of Essex, GB) [dblp]
  • Tom Schaul (Google DeepMind - London, GB) [dblp]
  • Noor Shaker (IT University of Copenhagen, DK) [dblp]
  • Moshe Sipper (Ben Gurion University - Beer Sheva, IL) [dblp]
  • Adam M. Smith (University of Washington - Seattle, US) [dblp]
  • Gillian Smith (Northeastern University - Boston, US) [dblp]
  • Pieter Spronck (Tilburg University, NL) [dblp]
  • Kenneth O. Stanley (University of Central Florida - Orlando, US) [dblp]
  • Ruck Thawonmas (Ritsumeikan University - Shiga, JP) [dblp]
  • Tommy Thompson (The University of Derby, GB) [dblp]
  • Julian Togelius (New York University, US) [dblp]
  • Michael Treanor (American University - Washington, US) [dblp]
  • Marc van Kreveld (Utrecht University, NL) [dblp]
  • Mark Winands (Maastricht University, NL) [dblp]
  • Georgios N. Yannakakis (University of Malta, MT) [dblp]
  • R. Michael Young (North Carolina State University - Raleigh, US) [dblp]
  • Fabio Zambetta (RMIT University - Melbourne, AU) [dblp]
  • Jichen Zhu (Drexel Univ. - Philadelphia, US) [dblp]
  • Alex Zook (Georgia Institute of Technology - Atlanta, US) [dblp]

Verwandte Seminare
  • Dagstuhl-Seminar 12191: Artificial and Computational Intelligence in Games (2012-05-06 - 2012-05-11) (Details)
  • Dagstuhl-Seminar 17471: Artificial and Computational Intelligence in Games: AI-Driven Game Design (2017-11-19 - 2017-11-24) (Details)
  • Dagstuhl-Seminar 19511: Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (2019-12-15 - 2019-12-20) (Details)
  • Dagstuhl-Seminar 22251: Human-Game AI Interaction (2022-06-19 - 2022-06-24) (Details)

Klassifikation
  • artificial intelligence / robotics
  • multimedia
  • soft computing / evolutionary algorithms

Schlagworte
  • Experimental analysis
  • meta-heuristics
  • multi-agent systems
  • dynamical systems
  • efficient algorithms
  • entertainment modeling
  • player satisfaction
  • game design
  • serious games
  • game theory