Name
Abstract | ||
|---|---|---|
Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXP- Complete complexity hinders their scalability and application in real-world domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than exist- ing algorithms while achieving comparable, or even superior, solution quality. |
Year | Venue | Keywords |
|---|---|---|
2009 | ICAPS | pomdp,uncertainty |
Field | DocType | Citations |
Partially observable Markov decision process,Computer science,Artificial intelligence,Solver,Novelty,Machine learning,Scalability | Conference | 35 |
PageRank | References | Authors |
1.44 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pradeep Varakantham | 1 | 648 | 63.05 |
| Jun-young Kwak | 2 | 74 | 6.96 |
| Matthew E. Taylor | 3 | 1352 | 94.88 |
| Janusz Marecki | 4 | 685 | 49.06 |
| Paul Scerri | 5 | 822 | 72.05 |
| Milind Tambe | 6 | 6008 | 522.25 |