Practical assumptions for planning under uncertainty
Autor(en): | Saborío, J.C. Hertzberg, J. |
Herausgeber: | van den Herik, J. Rocha, A.P. Filipe, J. |
Stichwörter: | Decision making; Decision making under uncertainty; Domain knowledge; Intelligent agents; Plan-based Robot Control; Planning Under Uncertainty; Practical problems; Problem Solving; Quality criteria; Robot controls; Robot programming, Context sensitive; Standard model, Problem solving | Erscheinungsdatum: | 2017 | Herausgeber: | SciTePress | Enthalten in: | ICAART 2017 - Proceedings of the 9th International Conference on Agents and Artificial Intelligence | Band: | 2 | Startseite: | 497 | Seitenende: | 502 | Zusammenfassung: | The (PO)MDP framework is a standard model in planning and decision-making under uncertainty, but the complexity of its methods makes it impractical for any reasonably large problem. In addition, task-planning demands solutions satisfying efficiency and quality criteria, often unachievable through optimizing methods. We propose an approach to planning that postpones optimality in favor of faster, satisficing behavior, supported by context-sensitive assumptions that allow an agent to reduce the dimensionality of its decision problems.We argue that a practical problem solving agent may sometimes assume full observability and determinism, based on generalizations, domain knowledge and an attentional filter obtained through a formal understanding of "relevance", therefore exploiting the structure of problems and not just their representations. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. |
Beschreibung: | Conference of 9th International Conference on Agents and Artificial Intelligence, ICAART 2017 ; Conference Date: 24 February 2017 Through 26 February 2017; Conference Code:134797 |
ISBN: | 9789897582202 | DOI: | 10.5220/0006189004970502 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068730573&doi=10.5220%2f0006189004970502&partnerID=40&md5=1c0df4f8aa02dd70890498f28f8c2b7d |
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geprüft am 07.06.2024