Abstract. In games with imperfect information, the 'information set' is a collection of all possible game histories that are consistent with, or explain, a player's observations. Current game playing systems rely on these best guesses of the true, partially-observable game as the foundation of their decision making, yet finding these information sets is expensive.We apply reactive Answer Set Programming (ASP) to the problem of sampling information sets in the field of General Game Playing. Furthermore, we use this domain as a test bed for evaluating the effectiveness of oClingo, a reactive answer set solver, in avoiding redundant search by keeping learnt clauses during incremental solving.

Abstract. The online answer set solver oClingo offers a powerful new technique for uniting the speed of Answer Set Programming (ASP) with dynamic events. The price of this power is paid by increased constraints on the construction of a 'safe' program-one that satisfies an arcane modularity condition. We provide an alternative in the form of so-called Agent Logic Programs-a concise declarative language for describing agent control strategies. Specifically, we take an ASPcompatible subset of Agent Logic Programs, extend it with exogenous actions, argue this translation is faithful to the original definition, and prove that it guarantees oClingo's modularity condition. The result is a safe, clean input language for oClingo and a new implementation for Agent Logic Programs.

General Game Playing is the design of AI systems able to understand the rules of new games and to use such descriptions to play those games effectively. Games with imperfectinformation have recently been added as a new challenge forexisting general game-playing systems. The HyperPlay technique presents a solution to this challenge by maintaining a collection of models of the true game as a foundation for reasoning, and move selection. The technique provides existing game players with a bolt-on solution to convert from perfect-information games to imperfect-information games. In this paper we describe the HyperPlay technique, show how it was adapted for use with a Monte Carlo decision making process and give experimental results for its performance.

No abstract