{"type":"ScholarlyArticle","title":"Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints","authors":[{"name":"Blai Bonet","slug":"blai-bonet","given":"Blai","family":"Bonet","affiliation":"Univ. Simón Bolívar Caracas , Venezuela"},{"name":"Giuseppe De Giacomo","slug":"giuseppe-de-giacomo","given":"Giuseppe","family":"De Giacomo","affiliation":"Sapienza Univ. Roma Rome , Italy"},{"name":"Hector Geffner","slug":"hector-geffner","given":"Hector","family":"Geffner","affiliation":"ICREA Univ. Pompeu Fabra Barcelona , Spain"},{"name":"Sasha Rubin","slug":"sasha-rubin","given":"Sasha","family":"Rubin","affiliation":"Univ. Federico II Naples , Italy"}],"abstract":"We study the characterization and computation of general policies for families of problems that share a  structure characterized by a common reduction into a single abstract problem. Policies mu that solve the abstract problem P have been shown to solve all problems Q that reduce to P provided that mu terminates in Q. In this work, we shed light on  why this termination condition is needed  and how it can be removed. The key observation is that the abstract problem P captures the common  structure among the concrete problems Q that is local (Markovian) but misses common structure that is global. We show how such global structure can be captured by means of trajectory constraints that in many cases can be expressed  as LTL formulas, thus reducing  generalized planning to LTL synthesis.  Moreover, for a broad class of problems that involve integer variables that can be increased or decreased, trajectory constraints can be compiled away, reducing generalized planning to fully observable non-deterministic planning.","doi":"10.24963/ijcai.2017/121","url":"https://ijcai.org/proceedings/2017/121","pdf":"https://ijcai.org/proceedings/2017/0121.pdf","markdown":"https://ijcai.org/proceedings/2017/121.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/121","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":121,"pages":{"first":873,"last":879},"keywords":["Knowledge Representation, Reasoning, and Logic: Action, Change and Causality","Planning and Scheduling: Theoretical Foundations of Planning","Agent-based and Multi-agent Systems: Formal verification, validation and synthesis"],"videos":[],"references":[{"position":1,"raw":"[Belle and Levesque, 2016] Vaishak Belle and Hector J. 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