Relational Markov games. (English) Zbl 1111.91305

Finzi, Alberto; Lukasiewicz, Thomas

Alferes, José Júlio (ed.) et al., Logics in artificial intelligence. 9th European conference, JELIA 2004, Lisbon, Portugal, September 27–30, 2004. Proceedings. Berlin: Springer (ISBN 3-540-23242-7/pbk). Lecture Notes in Computer Science 3229. Lecture Notes in Artificial Intelligence, 320-333 (2004).

Summary: Towards a compact and elaboration-tolerant first-order representation of Markov games, we introduce 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. We also present a symbolic value iteration algorithm for computing Nash policy pairs in this framework.
For the entire collection see [Zbl 1056.68002].

MSC:

91A15	Stochastic games, stochastic differential games
68T27	Logic in artificial intelligence