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1. ProcDefense — A Game Framework for Procedural Player Skill Training

   Brandon R. Thorne, Hiru Nelakkutti (January 2017, Experimental AI in Games Workshop at the Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference)

   A challenge of game design is in providing affordances to players so that they can learn and improve their skills. Ad- vances in Procedural Content Generation (PCG) suggest this type of game content is a candidate for automatic creation. Some work in PCG has been successful in customizing game difficulty to achieve desired player experience; however, this often involves bringing the difficulty of the game to a level appropriate for the player’s current skills. Players desiring to improve their performance in a particular game may be will- ing to tolerate relatively higher levels of frustration and anx- iety than are targeted in experience-based approaches. As an initial step in this line of inquiry, we introduce ProcDefense, an action game with a modular difficulty control interface, as a platform for future inquiry into the effectiveness of differing PCG techniques for performance-training, dynamic difficulty adjustment. 

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2. Superhuman AI for multiplayer poker

   https://doi.org/10.1126/science.aay2400  

   Brown, Noam; Sandholm, Tuomas (August 2019, Science)

   In recent years there have been great strides in artificial intelligence (AI), with games often serving as challenge problems, benchmarks, and milestones for progress. Poker has served for decades as such a challenge problem. Past successes in such benchmarks, including poker, have been limited to two-player games. However, poker in particular is traditionally played with more than two players. Multiplayer games present fundamental additional issues beyond those in two-player games, and multiplayer poker is a recognized AI milestone. In this paper we present Pluribus, an AI that we show is stronger than top human professionals in six-player no-limit Texas hold’em poker, the most popular form of poker played by humans. 

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3. On the Modeling and Agent-Based Simulation of a Cooperative Group Anagram Game

   Hu, Zhihao; Deng, Xinwei; Goode, Brian J.; Ramakrishnan, Naren; Saraf, Parang; Self, Nathan; Adiga, Abhijin; Korkmaz, Gizem; Kuhlman, Chris J.; Machi, Dustin; et al (January 2019, Winter Simulation Conference)

   Anagram games (i.e., word construction games in which players use letters to form words) have been researched for some 60 years. Games with individual players are the subject of over 20 published investigations. Moreover, there are many popular commercial anagram games such as Scrabble. Recently, cooperative team play of anagram games has been studied experimentally. With all of the experimental work and the popularity of such games, it is somewhat surprising that very little modeling of anagram games has been done to predict player behavior/actions in them. We devise a cooperative group anagram game and develop an agent-based modeling and simulation framework to capture player interactions of sharing letters and forming words. Our primary goals are to understand, quantitatively predict, and explain individual and aggregate group behavior, through simulations, to inform the design of a group anagram game experimental platform. 

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4. Hindsight and Sequential Rationality of Correlated Play

   Morrill, D; D'Orazio, R; Sarfati, R; Lanctot, M; Wright, J.R.; Greenwald, A.R; Bowling, M. (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Driven by recent successes in two-player, zero-sum game solving and playing, artificial intelligence work on games has increasingly focused on algorithms that produce equilibrium-based strategies. However, this approach has been less effective at producing competent players in general-sum games or those with more than two players than in two-player, zero-sum games. An appealing alternative is to consider adaptive algorithms that ensure strong performance in hindsight relative to what could have been achieved with modified behavior. This approach also leads to a game-theoretic analysis, but in the correlated play that arises from joint learning dynamics rather than factored agent behavior at equilibrium. We develop and advocate for this hindsight rationality framing of learning in general sequential decision-making settings. To this end, we re-examine mediated equilibrium and deviation types in extensive-form games, thereby gaining a more complete understanding and resolving past misconceptions. We present a set of examples illustrating the distinct strengths and weaknesses of each type of equilibrium in the literature, and prove that no tractable concept subsumes all others. This line of inquiry culminates in the definition of the deviation and equilibrium classes that correspond to algorithms in the counterfactual regret minimization (CFR) family, relating them to all others in the literature. Examining CFR in greater detail further leads to a new recursive definition of rationality in correlated play that extends sequential rationality in a way that naturally applies to hindsight evaluation. 

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5. Experience Management in Multi-player Games

   Zhu, Jichen; Ontañón, Santiago (January 2019, Proceedings of the 2019 IEEE Conference on Games)

   Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM. 

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