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 (nonprobabilistic) 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.

Imperfect-information games and generalized planning / DE GIACOMO, Giuseppe; Murano, Aniello; Rubin, Sasha; Di Stasio, Antonio. - STAMPA. - (2016), pp. 1037-1043. (Intervento presentato al convegno 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 tenutosi a New York; United States).

Imperfect-information games and generalized planning

DE GIACOMO, Giuseppe
;
Di Stasio, Antonio
2016

Abstract

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 (nonprobabilistic) 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.
2016
25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Artificial Intelligence
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Imperfect-information games and generalized planning / DE GIACOMO, Giuseppe; Murano, Aniello; Rubin, Sasha; Di Stasio, Antonio. - STAMPA. - (2016), pp. 1037-1043. (Intervento presentato al convegno 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 tenutosi a New York; United States).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/950780
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