{"type":"ScholarlyArticle","title":"Real-Time Heuristic Search with LTLf Goals","authors":[{"name":"Jaime Middleton","slug":"jaime-middleton","given":"Jaime","family":"Middleton","affiliation":"Department of Computer Science , Pontificia Universidad Católica de Chile , Santiago , Chile; Tucar SpA , Santiago , Chile"},{"name":"Rodrigo Toro Icarte","slug":"rodrigo-toro-icarte","given":"Rodrigo Toro","family":"Icarte","affiliation":"Department of Computer Science , Pontificia Universidad Católica de Chile , Santiago , Chile; Centro Nacional de Inteligencia Artificial CENIA , Santiago , Chile"},{"name":"Jorge Baier","slug":"jorge-baier","given":"Jorge","family":"Baier","affiliation":"Department of Computer Science , Pontificia Universidad Católica de Chile , Santiago , Chile; Centro Nacional de Inteligencia Artificial CENIA , Santiago , Chile; Instituto Milenio Fundamentos de los Datos , Santiago , Chile"}],"abstract":"In Real-Time Heuristic Search (RTHS) we are given a search graph G, a heuristic, and the objective is to find a path from a given start node to a given goal node in G. As such, one does not impose any trajectory constraints on the path, besides reaching the goal. In this paper we consider a version of RTHS in which temporally extended goals can be defined on the form of the path. Such goals are specified in Linear Temporal Logic over Finite Traces (LTLf), an expressive language that has been considered in many other frameworks, such as Automated Planning, Synthesis, and Reinforcement Learning, but has not yet been studied in the context of RTHS. We propose a general automata-theoretic approach for RTHS, whereby LTLf goals are supported as the result of searching over the cross product of the search graph and the automaton for the LTLf goal; specifically, we describe LTL-LRTA*, a version of LSS-LRTA*. Second, we propose an approach to produce heuristics for LTLf goals, based on existing goal-dependent heuristics. Finally, we propose a greedy strategy for RTHS with LTLf goals, which focuses search to make progress over the structure of the automaton; this yields LTL-LRTA*+A. In our experimental evaluation over standard benchmarks we show LTL-LRTA*+A may outperform LTL-LRTA* substantially for a variety of LTLf goals.","doi":"10.24963/ijcai.2022/663","url":"https://ijcai.org/proceedings/2022/663","pdf":"https://ijcai.org/proceedings/2022/0663.pdf","markdown":"https://ijcai.org/proceedings/2022/663.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/663","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Search","track":"Main Track","number":663,"pages":{"first":4785,"last":4792},"keywords":["Search: Local search","Uncertainty in AI: Sequential Decision Making","Knowledge Representation and Reasoning: Knowledge Representation Languages","Search: Combinatorial Search and Optimisation","Search: Heuristic Search"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986914","length_sec":82,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985057","length_sec":326,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bacchus et al., 1997] Fahiem Bacchus, Craig Boutilier, and Adam J. 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