Abstract
Nowadays, multiagent planning under uncertainty scales to tens or even hundreds of agents. However, current methods either are restricted to problems with factored value functions, or provide solutions without any guarantees on quality. Methods in the former category typically build on heuristic search using upper bounds on the value function. Unfortunately, no techniques exist to compute such upper bounds for problems with non-factored value functions, which would additionally allow for meaningful benchmarking of methods of the latter category. To mitigate this problem, this paper introduces a family of influence-optimistic upper bounds for factored Dec-POMDPs without factored value functions. We demonstrate how we can achieve firm quality guarantees for problems with hundreds of agents.
Original language | English |
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Title of host publication | IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence |
Publisher | International Joint Conferences on Artificial Intelligence (IJCAI) |
Pages | 1645-1651 |
Number of pages | 7 |
Volume | 2015-January |
ISBN (Electronic) | 9781577357384 |
Publication status | Published - 1 Jan 2015 |
Event | IJCAI 2015: 24th International Joint Conference on Artificial Intelligence - Buenos Aires, Argentina Duration: 25 Jul 2015 → 31 Jul 2015 Conference number: 24 |
Conference
Conference | IJCAI 2015 |
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Country/Territory | Argentina |
City | Buenos Aires |
Period | 25/07/15 → 31/07/15 |