Lauri, Mikko ; Pajarinen, Joni ; Peters, Jan (2019)
Information Gathering in Decentralized POMDPs by Policy Graph Improvement.
18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). Montreal, Kanada (13.-17.05.2019)
Konferenzveröffentlichung, Bibliographie
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Kurzbeschreibung (Abstract)
Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest without the ability to communicate. Decentralized partially observable Markov decision processes (Dec-POMDPs) are a general, principled model well-suited for such decentralized multiagent decision-making problems. In this paper, we investigate Dec-POMDPs for decentralized information gathering problems. An optimal solution of a Dec-POMDP maximizes the expected sum of rewards over time. To encourage information gathering, we set the reward as a function of the agents’ state information, for example the negative Shannon entropy. We prove that if the reward is convex, then the finite-horizon value function of the corresponding Dec-POMDP is also convex. We propose the first heuristic algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving problems an order of magnitude larger than previous state-of-the-art.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Lauri, Mikko ; Pajarinen, Joni ; Peters, Jan |
| Art des Eintrags: | Bibliographie |
| Titel: | Information Gathering in Decentralized POMDPs by Policy Graph Improvement |
| Sprache: | Englisch |
| Publikationsjahr: | Mai 2019 |
| Ort: | Darmstadt |
| Verlag: | International Foundation for Autonomous Agents and Multiagent Systems |
| Buchtitel: | Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems |
| Veranstaltungstitel: | 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) |
| Veranstaltungsort: | Montreal, Kanada |
| Veranstaltungsdatum: | 13.-17.05.2019 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest without the ability to communicate. Decentralized partially observable Markov decision processes (Dec-POMDPs) are a general, principled model well-suited for such decentralized multiagent decision-making problems. In this paper, we investigate Dec-POMDPs for decentralized information gathering problems. An optimal solution of a Dec-POMDP maximizes the expected sum of rewards over time. To encourage information gathering, we set the reward as a function of the agents’ state information, for example the negative Shannon entropy. We prove that if the reward is convex, then the finite-horizon value function of the corresponding Dec-POMDP is also convex. We propose the first heuristic algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving problems an order of magnitude larger than previous state-of-the-art. |
| Freie Schlagworte: | decentralized POMDPs, multi-agent planning, planning under uncertainty, information theory |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| TU-Projekte: | EC/H2020|640554|SKILLS4ROBOTS |
| Hinterlegungsdatum: | 02 Aug 2024 13:15 |
| Letzte Änderung: | 02 Aug 2024 13:15 |
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Verfügbare Versionen dieses Eintrags
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Information Gathering in Decentralized POMDPs by Policy Graph Improvement. (deposited 17 Okt 2023 11:34)
- Information Gathering in Decentralized POMDPs by Policy Graph Improvement. (deposited 02 Aug 2024 13:15) [Gegenwärtig angezeigt]
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