{"type":"ScholarlyArticle","title":"Counterplanning using Goal Recognition and Landmarks","authors":[{"name":"Alberto Pozanco","slug":"alberto-pozanco","given":"Alberto","family":"Pozanco","affiliation":"Departamento de Informática , Universidad Carlos III de Madrid Avda. de la Universidad , 30. 28911 Leganés , Madrid , Spain"},{"name":"Yolanda E-Martín","slug":"yolanda-e-martin","given":"Yolanda","family":"E-Martín","affiliation":"Departamento de Informática , Universidad Carlos III de Madrid Avda. de la Universidad , 30. 28911 Leganés , Madrid , Spain"},{"name":"Susana Fernández","slug":"susana-fernandez","given":"Susana","family":"Fernández","affiliation":"Departamento de Informática , Universidad Carlos III de Madrid Avda. de la Universidad , 30. 28911 Leganés , Madrid , Spain"},{"name":"Daniel Borrajo","slug":"daniel-borrajo","given":"Daniel","family":"Borrajo","affiliation":"Departamento de Informática , Universidad Carlos III de Madrid Avda. de la Universidad , 30. 28911 Leganés , Madrid , Spain"}],"abstract":"In non-cooperative multi-agent systems, agents might want to prevent the opponents from achieving their goals. One alternative to solve this task would be using counterplanning to generate a plan that allows an agent to block other's to reach their goals. In this paper, we introduce a fully automated domain-independent approach for counterplanning. It combines; goal recognition to infer an opponent's goal; landmarks' computation to identify subgoals that can be used to block opponents' goals achievement; and classical automated planning to generate plans that prevent the opponent's goals achievement. Experimental results in several domains show the benefits of our novel approach.","doi":"10.24963/ijcai.2018/668","url":"https://ijcai.org/proceedings/2018/668","pdf":"https://ijcai.org/proceedings/2018/0668.pdf","markdown":"https://ijcai.org/proceedings/2018/668.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/668","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Planning and Scheduling","track":"Main track","number":668,"pages":{"first":4808,"last":4814},"keywords":["Planning and Scheduling: Planning and Scheduling","Agent-based and Multi-agent Systems: Noncooperative Games","Planning and Scheduling: Distributed;Multi-agent Planning","Agent-based and Multi-agent Systems: Multi-agent Planning","Planning and Scheduling: Activity and Plan Recognition"],"videos":[],"references":[{"position":1,"raw":"[Albrecht et al., 1997] David W. 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