Efficient multi-agent epistemic planning: Teaching planners about nested belief
| Autor: | Sheila A. McIlraith, Tim Miller, Christian Muise, Liz Sonenberg, Vaishak Belle, Adrian R. Pearce, Paolo Felli |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
I.2 automated planning Linguistics and Language Computer Science - Artificial Intelligence Computer science Autonomous agent 02 engineering and technology Plan (drawing) 01 natural sciences Language and Linguistics Task (project management) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Single agent 0101 mathematics epistemic planning Management science Field (Bourdieu) 68T42 010102 general mathematics Perspective (graphical) knowledge and belief Artificial Intelligence (cs.AI) 020201 artificial intelligence & image processing Applications of artificial intelligence |
| Zdroj: | Muise, C, Belle, V, Felli, P, McIlraith, S, Miller, T, Pearce, A R & Sonenberg, L 2022, ' Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief ', Artificial Intelligence, vol. 302, 103605 . https://doi.org/10.1016/j.artint.2021.103605 |
| ISSN: | 0004-3702 |
| DOI: | 10.1016/j.artint.2021.103605 |
| Popis: | Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents. Published in Special Issue of the Artificial Intelligence Journal (AIJ) on Epistemic Planning |
| Databáze: | OpenAIRE |
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