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- Zeitschriftenartikel2018 IEEE Conference on Computational Intelligence and Games (CIG 2018)(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Winands, Mark H. M.
- ZeitschriftenartikelA Games Industry Perspective on Recent Game AI Developments(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Preuss, Mike; Risi, Sebastian
- ZeitschriftenartikelAI for Ancient Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Browne, CameronThis report summarises the Digital Ludeme Project, a recently launched 5-year research project being conducted at Maastricht University. This computational study of the world’s traditional strategy games seeks to improve our understanding of early games, their development, and their role in the spread of related mathematical ideas throughout recorded human history.
- ZeitschriftenartikelArtificial Intelligence and Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Lucas, Simon
- ZeitschriftenartikelBehind DeepMind’s AlphaStar AI that Reached Grandmaster Level in StarCraft II(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, Mike
- ZeitschriftenartikelExplaining AI: Are We Ready For It?(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Wrede, Britta
- ZeitschriftenartikelExtensional Paramodulation for Higher-Order Logic and Its Effective Implementation Leo-III(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Steen, AlexanderAutomation of classical higher-order logic faces various theoretical and practical challenges. On a theoretical level, powerful calculi for effective equality reasoning from first-order theorem proving cannot be lifted to the higher-order domain in a simple manner. Practically, implementations of higher-order reasoning systems have to incorporate procedures that often have high time complexity or are not decidable in general. In my dissertation, both the theoretical and the practical challenges of designing an effective higher-order reasoning system are studied. The resulting system, the automated theorem prover Leo-III, is one of the most effective and versatile systems, in terms of supported logical formalisms, to date.
- ZeitschriftenartikelFormation of a Research Discipline Artificial Intelligence and Intellectics at the Technical University of Munich(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Bibel, Wolfgang; Furbach, UlrichAcademic Computer Science emerged in Germany at the end of the 1960s. In 2017, the Munich universities celebrated “50 Years of Computer Science in Munich”. To this occasion various events were held; also, there was a special issue in the Informatik Spektrum, the official journal of the Gesellschaft für Informatik e.V. (GI – the German Society for Computer Science) and of associated organizations, as well as an anthology on Computer Science in Munich [ 1 ]. One year later, the present authors published a tribute to the research group for Artificial Intelligence/Intellectics at the TUM in a volume of the Deutsche Museum’s Preprints series [ 2 ], of which the present article is a very brief summary—for much more detailed information and impressions of former group members please refer to this booklet. The Munich group for Artificial Intelligence/Intellectics came into being thanks to academic freedom at German universities, in this case the Technical University of Munich (TUM): A single young scientist is enthusiastic about an idea, a new idea, which has not yet been worked on or supported by any professor at the TUM: Artificial Intelligence or Intellectics. The scientist initiates relationships with other colleagues, nationally and internationally; he is successful, receives research funding, and establishes a research group that asserted itself over almost four decades and influenced and advanced the field. The present article provides a brief history of the group.
- ZeitschriftenartikelFrom Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Risi, Sebastian; Preuss, MikeThis paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.
- ZeitschriftenartikelI Feel I Feel You: A Theory of Mind Experiment in Games(KI - Künstliche Intelligenz: Vol. 34, No. 1, 2020) Melhart, David; Yannakakis, Georgios N.; Liapis, AntoniosIn this study into the player’s emotional theory of mind (ToM) of gameplaying agents, we investigate how an agent’s behaviour and the player’s own performance and emotions shape the recognition of a frustrated behaviour. We focus on the perception of frustration as it is a prevalent affective experience in human-computer interaction. We present a testbed game tailored towards this end, in which a player competes against an agent with a frustration model based on theory. We collect gameplay data, an annotated ground truth about the player’s appraisal of the agent’s frustration, and apply face recognition to estimate the player’s emotional state. We examine the collected data through correlation analysis and predictive machine learning models, and find that the player’s observable emotions are not correlated highly with the perceived frustration of the agent. This suggests that our subject’s ToM is a cognitive process based on the gameplay context. Our predictive models—using ranking support vector machines—corroborate these results, yielding moderately accurate predictors of players’ ToM.