Complexity of multi-agent conformant planning with group knowledge.
In this paper, we propose a dynamic epistemic framework to capture the knowledge evolution in multi-agent systems where agents are not able to observe. We formalize multi-agent conformant planning with group knowledge, and reduce planning problems to model checking problems. We prove that multi-agen...
| Publicado en: | Synthese Vol. 201; no. 4; pp. 1 - 31 |
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| Formato: | Artículo |
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Springer Nature
Apr2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=162829423&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 162829423 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Apr2023 vid: 201 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 162829423 10.1007/s11229-023-04095-5 ppf: 1 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P size: 633KB tig: atl: Complexity of multi-agent conformant planning with group knowledge. aug: au: Li, Yanjun affil: College of Philosophy, Nankai University, Tianjin, China sug: keyword: Common knowledge Conformant planning Distributed knowledge Multi-agent planning ab: In this paper, we propose a dynamic epistemic framework to capture the knowledge evolution in multi-agent systems where agents are not able to observe. We formalize multi-agent conformant planning with group knowledge, and reduce planning problems to model checking problems. We prove that multi-agent conformant planning with group knowledge is Pspace-complete on the size of dynamic epistemic models. We also consider the alternative Kripke semantics, and show that for each Kripke model with perfect recall and no miracles, there is an equivalent dynamic epistemic model and vice versa. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2023. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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