{"type":"ScholarlyArticle","title":"A General Multi-agent Epistemic Planner Based on Higher-order Belief Change","authors":[{"name":"Xiao Huang","slug":"xiao-huang","given":"Xiao","family":"Huang","affiliation":"Dept. of Computer Science , Sun Yat-sen University , Guangzhou 510006 , China"},{"name":"Biqing Fang","slug":"biqing-fang","given":"Biqing","family":"Fang","affiliation":"Dept. of Computer Science , Sun Yat-sen University , Guangzhou 510006 , China"},{"name":"Hai Wan","slug":"hai-wan","given":"Hai","family":"Wan","affiliation":"Dept. of Computer Science , Sun Yat-sen University , Guangzhou 510006 , China"},{"name":"Yongmei Liu","slug":"yongmei-liu","given":"Yongmei","family":"Liu","affiliation":"Dept. of Computer Science , Sun Yat-sen University , Guangzhou 510006 , China"}],"abstract":"In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we propose a general representation language for multi-agent epistemic planning where the initial KB and the goal, the preconditions and effects of actions can be arbitrary multi-agent epistemic formulas, and the solution is an action tree branching on sensing results.To support efficient reasoning in the multi-agent KD45 logic, we make use of a normal form called alternative cover disjunctive formula (ACDF). We propose basic revision and update algorithms for ACDF formulas. We also handle static propositional common knowledge, which we call constraints. Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MAEP. Our experimental results show the viability of our approach.","doi":"10.24963/ijcai.2017/152","url":"https://ijcai.org/proceedings/2017/152","pdf":"https://ijcai.org/proceedings/2017/0152.pdf","markdown":"https://ijcai.org/proceedings/2017/152.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/152","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":152,"pages":{"first":1093,"last":1101},"keywords":["Knowledge Representation, Reasoning, and Logic: Belief Change","Knowledge Representation, Reasoning, and Logic: Reasoning about Knowlege and Belief","Knowledge Representation, Reasoning, and Logic: Action, Change and Causality","Planning and Scheduling: Theoretical Foundations of Planning"],"videos":[],"references":[{"position":1,"raw":"[Alchourrón et al., 1985] Carlos E. 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