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Perfect recall is the common and natural assumption that an agent never forgets. As a consequence, the agent can always condition its choice of action on any prior observations. In this paper, we explore relaxing this assumption. We observe the negative impact this relaxation has on algorithms: some algorithms are no longer well-defined, while others lose(More)
Evaluating the performance of an agent or group of agents can be, by itself, a very challenging problem. The stochas-tic nature of the environment plus the stochastic nature of agents' decisions can result in estimates with intractably large variances. This paper examines the problem of finding low variance estimates of agent performance. In particular, we(More)
Poker games provide a useful testbed for modern Artificial Intelligence techniques. Unlike many classical game domains such as chess and checkers, poker includes elements of imperfect information , stochastic events, and one or more adversarial agents to interact with. Furthermore, in poker it is possible to win or lose by varying degrees. Therefore, it can(More)
— Researchers often have access to a variety of different high-performance computer (HPC) systems in different administrative domains, possibly across a wide-area network. Consequently, the security infrastructure becomes an important component of an overlay metacomputer: a user-level aggregation of HPC systems. The Grid Security Infrastructure (GSI) uses a(More)
We present an algorithm which determines the outcome of an arbitrary Hex game-state by finding a winning virtual connection for the winning player. Our algorithm performs a recursive descent search of the game-tree, combining fixed and dynamic game-state virtual connection composition rules with some new Hex game-state reduction results based on move(More)
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