Abstract
Towards a compact and elaboration-tolerant first-order representation of Markovgames,weintroduce relational Markov games,which combine standard Markov games with first-order action descriptions in a stochastic variant of the situation calculus. We focus on the zero-sum two-agent case, where we have two agents with diametrically opposed goals.Wealso presentasymbolic value iteration algorithm for computing Nash policy pairs in this framework.
Cite
CITATION STYLE
Finzi, A., & Lukasiewicz, T. (2004). Relational Markov games. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3229, pp. 320–333). Springer Verlag. https://doi.org/10.1007/978-3-540-30227-8_28
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