In two-player cooperative games, agents can play together effectively when they have accurate assumptions about how their teammate will behave, but may perform poorly when these assumptions are inaccurate. In language games, failure may be due to disagreement in the understanding of either the semantics or pragmatics of an utterance. We model coarse uncertainty in semantics using a prior distribution of language models and uncertainty in pragmatics using the cognitive hierarchy, combining the two aspects into a single prior distribution over possible partner types. Fine-grained uncertainty in semantics is modeled using noise that is added to the embeddings of words in the language. To handle all forms of uncertainty we construct agents that learn the behavior of their partner using Bayesian inference and use this information to maximize the expected value of a heuristic function. We test this approach by constructing Bayesian agents for the game of Codenames, and show that they perform better in experiments where semantics is uncertain

The game of Codenames has recently emerged as a domain of interest for intelligent agent design. The game is unique due to the way that language and coordination between teammates play important roles. Previous approaches to designing agents for this game have utilized a single internal language model to determine action choices. This often leads to good performance with some teammates and inferior performance with other teammates, as the agent cannot adapt to any specific teammate. In this paper we present the first adaptive agent for playing Codenames. We adopt an ensemble approach with the goal of determining, during the course of interacting with a specific teammate, which of our internal expert agents, each potentially with its own language model, is the best match. One difficulty faced in this approach is the lack of a single numerical metric that accurately captures the performance of a Codenames team. Prior Codenames research has utilized a handful of different metrics to evaluate agent teams. We propose a novel single metric to evaluate the performance of a Codenames team, whether playing a single team (solitaire) game, or a competitive game against another team. We then present and analyze an ensemble agent which selects an internal expert on each turn in order to maximize this proposed metric. Experimental analysis shows that this ensemble approach adapts to individual teammates and often performs nearly as well as the best internal expert with a teammate. Crucially, this success does not depend on any previous knowledge about the teammates, the ensemble agents, or their compatibility. This research represents an important step to making language-based agents for cooperative language settings like Codenames more adaptable to individual teammates.

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA.

Benjamin Grosof (Coherent Knowledge from episodic memory to great progress is being made on methods Systems) on representing activity create semantic memory, using a combination to solve problems related to structure context through semantic rule methods, of semantic memory and prediction, motion simulation, deriving from experience in the episodic memory to guide users?

This paper introduces Monte Carlo *-Minimax Search (MCMS), a Monte Carlo search algorithm for turned-based, stochastic, two-player, zero-sum games of perfect information. The algorithm is designed for the class of of densely stochastic games; that is, games where one would rarely expect to sample the same successor state multiple times at any particular chance node. Our approach combines sparse sampling techniques from MDP planning with classic pruning techniques developed for adversarial expectimax planning. We compare and contrast our algorithm to the traditional *-Minimax approaches, as well as MCTS enhanced with the Double Progressive Widening, on four games: Pig, EinStein Wurfelt Nicht!, Can't Stop, and Ra. Our results show that MCMS can be competitive with enhanced MCTS variants in some domains, while consistently outperforming the equivalent classic approaches given the same amount of thinking time.

An important aspect of agent evaluation in stochastic games, especially poker, is the need to reduce the outcome variance in order to get accurate and significant results. The current method used in the Annual Computer Poker Competition’s analysis is that of duplicate poker, an approach that leverages the ability to deal sets of cards to agents in order to reduce variance. This work explores a different approach to variance reduction by using a control variate based approach known as baseline. The baseline approach involves using an agent’s outcome in self play to create an unbiased estimator for use in agent evaluation and has been shown to work well in both poker and trading agent competition domains. Base- line does not require that the agents are able to be dealt sets of cards, making it a more robust technique than duplicate. This approach is compared to the current duplicate method, as well as other variations of duplicate poker on the results of the 2011 two player no-limit and three player limit Texas Hold’em ACPC tournaments.

Collusion is the practice of two or more parties deliberately cooperating to the detriment of others. While such behavior may be desirable in certain circumstances, in many it is considered dishonest and unfair. If agents otherwise hold strictly to the established rules, though, collusion can be challenging to police. In this paper, we introduce an automatic method for collusion detection in sequential games. We achieve this through a novel object, called a collusion table, that captures the effects of collusive behavior, i.e., advantage to the colluding parties, without assuming any particular pattern of behavior. We show the effectiveness of this method in the domain of poker, a popular game where collusion is prohibited.

Computational pool is a relatively recent entrant into the group of games played by computer agents. It features a unique combination of properties that distinguish it from oth- ers such games, including continuous action and state spaces, uncertainty in execution, a unique turn-taking structure, and of course an adversarial nature. This article discusses some of the work done to date, focusing on the software side of the pool-playing problem. We discuss in some depth CueCard, the program that won the 2008 computational pool tournament. Research questions and ideas spawned by work on this problem are also discussed. We close by announcing the 2011 computational pool tournament, which will take place in conjunction with the Twenty-Fifth AAAI Conference.