TY - GEN
T1 - Qualitative planning under partial observability in multi-agent domains
AU - Brafman, Ronen I.
AU - Shani, Guy
AU - Zilberstein, Shlomo
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Decentralized POMDPs (Dec-POMDPs) provide a rich, attractive model for planning under uncertainty and partial observability in cooperative multi-agent domains with a growing body of research. In this paper we formulate a qualitative, propositional model for multi-agent planning under uncertainty with partial observability, which we call Qualitative Dec-POMDP (QDec-POMDP). We show that the worst-case complexity of planning in QDec-POMDPs is similar to that of Dec-POMDPs. Still, because the model is more "classical" in nature, it is more compact and easier to specify. Furthermore, it eases the adaptation of methods used in classical and contingent planning to solve problems that challenge current Dec-POMDPs solvers. In particular, in this paper we describe a method based on compilation to classical planning, which handles multi-agent planning problems significantly larger than those handled by current Dec-POMDP algorithms.
AB - Decentralized POMDPs (Dec-POMDPs) provide a rich, attractive model for planning under uncertainty and partial observability in cooperative multi-agent domains with a growing body of research. In this paper we formulate a qualitative, propositional model for multi-agent planning under uncertainty with partial observability, which we call Qualitative Dec-POMDP (QDec-POMDP). We show that the worst-case complexity of planning in QDec-POMDPs is similar to that of Dec-POMDPs. Still, because the model is more "classical" in nature, it is more compact and easier to specify. Furthermore, it eases the adaptation of methods used in classical and contingent planning to solve problems that challenge current Dec-POMDPs solvers. In particular, in this paper we describe a method based on compilation to classical planning, which handles multi-agent planning problems significantly larger than those handled by current Dec-POMDP algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84893360793&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84893360793
SN - 9781577356158
T3 - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
SP - 130
EP - 137
BT - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
T2 - 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Y2 - 14 July 2013 through 18 July 2013
ER -