Planning under uncertainty through goal-driven action selection
Autor(en): | Saborío, J.C. Hertzberg, J. |
Herausgeber: | van den Herik, J. Rocha, A.P. |
Stichwörter: | Artificial intelligence, Action selection; Clear specifications; Interleaving planning and execution; Mobile robotic; On-line planning; Partial observability; Performance challenges; Planning under uncertainty, Robot programming | Erscheinungsdatum: | 2019 | Herausgeber: | Springer Verlag | Enthalten in: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Band: | 11352 LNAI | Startseite: | 182 | Seitenende: | 201 | Zusammenfassung: | Online planning in domains with uncertainty and partial observability conveys a series of performance challenges: agents must obtain information about the environment, quickly select actions with high reward prospects and avoid very expensive mistakes, while interleaving planning and execution in highly variable and uncertain domains. In order to reduce the amount of mistakes and help an agent focus on directly relevant actions, we propose a goal-driven, action selection method for planning in (PO)MDP's. This method introduces a reward bonus and a rollout policy for MCTS planners, both of which depend almost exclusively on a clear specification of the goal and produced promising results when planning in large domains of interest to cognitive and mobile robotics. © Springer Nature Switzerland AG 2019. |
Beschreibung: | Conference of 10th International Conference on Agents and Artificial Intelligence, ICAART 2018 ; Conference Date: 16 January 2018 Through 18 January 2018; Conference Code:222359 |
ISBN: | 9783030054526 | ISSN: | 03029743 | DOI: | 10.1007/978-3-030-05453-3_9 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059684528&doi=10.1007%2f978-3-030-05453-3_9&partnerID=40&md5=4a346931e3a4647f3b2dd90b3f561136 |
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