Strategy Inference in Stochastic Games Using Belief Networks
نویسنده
چکیده
In many gaming and real-world scenarios players try to predict the behavior of the other players. This assumes some underlying strategies that players follow, that they can be inferred, and that a reasonable player can counter them in real-time. This paper seeks to formulate a definition for strategies and their relationship with policies, determine the viability of inferring strategies, and formulate counter-strategies in real-time. If successful, players utilizing dominant strategies should defeat those using weaker strategies, the weaker strategies should be shifted to a better policy within that strategy, and this should result in improved performance. We will show that strategies offer significant performance enhancement, strategies can be recognized in real-time, and AI’s utilizing strategy inference will outperform their originally superior opponents.
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