Heuristic Search of Multiagent Influence Space (bibtex)
by Stefan Witwicki and Frans A. Oliehoek and Leslie P. Kaelbling
Abstract:
Multiagent planning under uncertainty has seen important progress in recent years. Two techniques, in particular, have substantially advanced efficiency and scalability of planning. Multiagent heuristic search gains traction by pruning large portions of the joint policy space deemed suboptimal by heuristic bounds. Alternatively, influence-based abstraction reformulates the search space of joint policies into a smaller space of influences, which represent the probabilistic effects that agents' policies may exert on one another. These techniques have been used independently, but never together, to solve solve larger problems (for Dec-POMDPs and subclasses) than was previously possible. In this paper, we take the logical albeit nontrivial next step of combining multiagent A* search and influence-based abstraction into a single algorithm. The mathematical foundation that we provide, such as partially-specified influence evaluation and admissible heuristic definition, enables an investigation into whether the two techniques bring complementary gains. Our empirical results indicate that A* can provide significant computational savings on top of those already afforded by influence-space search, thereby bringing a significant contribution to the field of multiagent planning under uncertainty.
Reference:
Heuristic Search of Multiagent Influence Space (Stefan Witwicki and Frans A. Oliehoek and Leslie P. Kaelbling), In Proceedings of The International Joint Conference on Autonomous Agents and Multi Agent Systems, 2012.
Bibtex Entry:
@InProceedings{Witwicki12AAMAS,
 author = {Stefan Witwicki and
 Frans A. Oliehoek and 
 Leslie P. Kaelbling},
 title = {Heuristic Search of Multiagent Influence Space},
 booktitle = {Proceedings of The International Joint Conference on Autonomous Agents and Multi Agent Systems},
 month = jun,
 year = 2012,
 OPTpages = {},
keywords={Multiagent},
 bib2html_rescat = {Multiagent systems - decentralized (approximate) planning under uncertainty},
 bib2html_pubtype = {Refereed Conference (International)},
 abstract =  {
 Multiagent planning under uncertainty has seen important progress in
 recent years. Two techniques, in particular, have substantially
 advanced efficiency and scalability of planning. Multiagent heuristic
 search gains traction by pruning large portions of the joint policy
 space deemed suboptimal by heuristic bounds. Alternatively,
 influence-based abstraction reformulates the search space of joint
 policies into a smaller space of influences, which represent the
 probabilistic effects that agents' policies may exert on one another.
 These techniques have been used independently, but never together, to
 solve solve larger problems (for Dec-POMDPs and subclasses) than was
 previously possible. In this paper, we take the logical albeit
 nontrivial next step of combining multiagent A* search and
 influence-based abstraction into a single algorithm. The mathematical
 foundation that we provide, such as partially-specified influence
 evaluation and admissible heuristic definition, enables an
 investigation into whether the two techniques bring complementary
 gains. Our empirical results indicate that A* can provide significant
 computational savings on top of those already afforded by
 influence-space search, thereby bringing a significant contribution to
 the field of multiagent planning under uncertainty.
 },
url={http://people.csail.mit.edu/fao/docs/Oliehoek11EUMAS.pdf}
}
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