Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Searching with probabilities
Multi-player alpha-beta pruning
Artificial Intelligence
A Bayesian approach to relevance in game playing
Artificial Intelligence - Special issue on relevance
Probabilistic opponent-model search
Information Sciences: an International Journal - Heuristic Search and Computer Game Playing
On Pruning Techniques for Multi-Player Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Robust game play against unknown opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Prob-Maxn: playing N-player games with opponent models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Pathology on game trees revisited, and an alternative to minimaxing
Artificial Intelligence
Mixing search strategies for multi-player games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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In binary-utility games, an agent can have only two possible utility values for final states, 1 (win) and 0 (lose). An adversarial binary-utility game is one where for each final state there must be at least one winning and one losing agent. We define an unbiased rational agent as one that seeks to maximize its utility value, but is equally likely to choose between states with the same utility value. This induces a probability distribution over the outcomes of the game, from which an agent can infer its probability to win. A single adversary binary game is one where there are only two possible outcomes, so that the winning probabilities remain binary values. In this case, the rational action for an agent is to play minimax. In this work we focus on the more complex, multiple-adversary environment. We propose a new algorithmic framework where agents try to maximize their winning probabilities. We begin by theoretically analyzing why an unbiased rational agent should take our approach in an unbounded environment and not that of the existing Paranoid or MaxN algorithms. We then expand our framework to a resource-bounded environment, where winning probabilities are estimated, and show empirical results supporting our claims.