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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)
Equilibrium or near-equilibrium solutions to very large extensive form games are often computed by using abstractions to reduce the game size. A common abstraction technique for games with a large number of available actions is to restrict the number of legal actions in every state. This method has been used to discover equilibrium solutions for the game of(More)
Extensive games can be used to model many scenarios in which multiple agents interact with an environment. There has been considerable recent research on finding strong strategies in very large, zero-sum extensive games. The standard approach in such work is to employ abstraction techniques to derive a more tractably sized game. An extensive game solver is(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)
This paper discusses the use of transfer entropy to infer relationships among entities. This is useful when one wants to understand relationships among entities but can only observe their behavior, but not direct interactions with one another. This is the kind of environment prevelant in network monitoring, where one can observe behavior coming into and(More)
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