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ATAL
2009
Springer

Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs

10 years 7 months ago
Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs
Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this family, MEMORY BOUNDED DYNAMIC PROGRAMMING (MBDP), which combines in a suitable manner top-down heuristics and bottom-up value function updates, can solve DEC-POMDPs with large horizons. The performances of MBDP, can be, however, drastically improved by avoiding the systematic generation and evaluation of all possible policies which result from the exhaustive backup. To achieve that, we suggest a heuristic search method, namely POINT BASED INCREMENTAL PRUNING (PBIP), which is able to distinguish policies with different heuristic estimates. Taking this insight into account, PBIP searches only among the most promising policies, finds those useful, and prunes dominated ones. Doing so permits us to reduce clearly the amount of computation required by the exhaustive backup. The computation experiment shows that PBI...
Jilles Steeve Dibangoye, Abdel-Illah Mouaddib, Bra
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ATAL
Authors Jilles Steeve Dibangoye, Abdel-Illah Mouaddib, Brahim Chaib-draa
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