{"type":"ScholarlyArticle","title":"A Survey on Model-Free Goal Recognition","authors":[{"name":"Leonardo Amado","slug":"leonardo-amado","given":"Leonardo","family":"Amado","affiliation":"University of Aberdeen , Scotland , UK"},{"name":"Sveta Paster Shainkopf","slug":"sveta-paster-shainkopf","given":"Sveta","family":"Paster Shainkopf","affiliation":""},{"name":"Ramon Fraga Pereira","slug":"ramon-fraga-pereira","given":"Ramon","family":"Fraga Pereira","affiliation":"University of Manchester , England , UK"},{"name":"Reuth Mirsky","slug":"reuth-mirsky","given":"Reuth","family":"Mirsky","affiliation":"Bar-Ilan University , Israel"},{"name":"Felipe Meneguzzi","slug":"felipe-meneguzzi","given":"Felipe","family":"Meneguzzi","affiliation":"University of Aberdeen , Scotland , UK"}],"abstract":"Goal Recognition is the task of inferring an agent's intentions from a set of observations. \r\nExisting recognition approaches have made considerable advances in domains such as human-robot interaction, intelligent tutoring systems, and surveillance. However, most approaches rely on explicit domain knowledge, often defined by a domain expert. Much recent research focus on mitigating the need for a domain expert while maintaining the ability to perform quality recognition, leading researchers to explore Model-Free Goal Recognition approaches. We comprehensively survey Model-Free Goal Recognition, and provide a perspective on the state-of-the-art approaches and their applications, showing recent advances. We categorize different approaches, introducing a taxonomy with a focus on their characteristics,  strengths, weaknesses, and suitability for different scenarios. We compare the advances each approach made to the state-of-the-art and provide a direction for future research in Model-Free Goal Recognition.","doi":"10.24963/ijcai.2024/877","url":"https://ijcai.org/proceedings/2024/877","pdf":"https://ijcai.org/proceedings/2024/0877.pdf","markdown":"https://ijcai.org/proceedings/2024/877.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/877","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":877,"pages":{"first":7923,"last":7931},"keywords":["Planning and Scheduling: PS: Activity and plan recognition"],"videos":[],"references":[{"position":1,"raw":"[Aineto et al., 2018] Diego Aineto, Sergio Jiménez, and Eva Onaindia. 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