Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Imperfect-Information Games and Generalized Planning / 1037
Giuseppe De Giacomo, Aniello Murano, Sasha Rubin, Antonio Di Stasio

We study a generalized form of planning under partial observability, in which we have multiple, possibly infinitely many, planning domains with the same actions and observations, and goals expressed over observations, which are possibly temporally extended. By building on work on two-player (non-probabilistic) games with imperfect information in the Formal Methods literature, we devise a general technique, generalizing the belief-state construction, to remove partial observability. This reduces the planning problem to a game of perfect information with a tight correspondence between plans and strategies. Then we instantiate the technique and solve some generalized-planning problems.