{"type":"ScholarlyArticle","title":"Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue","authors":[{"name":"Cristian-Paul Bara","slug":"cristian-paul-bara","given":"Cristian-Paul","family":"Bara","affiliation":"University of Michigan"},{"name":"Ziqiao Ma","slug":"ziqiao-ma","given":"Ziqiao","family":"Ma","affiliation":"University of Michigan"},{"name":"Yingzhuo Yu","slug":"yingzhuo-yu","given":"Yingzhuo","family":"Yu","affiliation":"University of Michigan"},{"name":"Julie Shah","slug":"julie-shah","given":"Julie","family":"Shah","affiliation":"Massachussets Institute of Technology"},{"name":"Joyce Chai","slug":"joyce-chai","given":"Joyce","family":"Chai","affiliation":"University of Michigan"}],"abstract":"Collaborative tasks often begin with partial task knowledge and incomplete plans from each partner. \r\nTo complete these tasks, partners need to engage in situated communication with their partners and coordinate their partial plans towards a complete plan to achieve a joint task goal. \r\nWhile such collaboration seems effortless in a human-human team, it is highly challenging for human-AI collaboration. \r\nTo address this limitation, this paper takes a step towards Collaborative Plan Acquisition, where humans and agents strive to learn and communicate with each other to acquire a complete plan for joint tasks. \r\nSpecifically, we formulate a novel problem for agents to predict the missing task knowledge for themselves and for their partners based on rich perceptual and dialogue history. \r\nWe extend a situated dialogue benchmark for symmetric collaborative tasks in a 3D blocks world and investigate computational strategies for plan acquisition. \r\nOur empirical results suggest that predicting the partner's missing knowledge is a more viable approach than predicting one's own. \r\nWe show that explicit modeling of the partner's dialogue moves and mental states produces improved and more stable results than without.\r\nThese results provide insight for future AI agents that \r\ncan predict what knowledge their partner is missing and, therefore, can proactively communicate such information to help the partner acquire such missing knowledge toward a common understanding of joint tasks.","doi":"10.24963/ijcai.2023/330","url":"https://ijcai.org/proceedings/2023/330","pdf":"https://ijcai.org/proceedings/2023/0330.pdf","markdown":"https://ijcai.org/proceedings/2023/330.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/330","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Humans and AI","track":"Main Track","number":330,"pages":{"first":2958,"last":2966},"keywords":["Humans and AI: HAI: Human-AI collaboration","Agent-based and Multi-agent Systems: MAS: Multi-agent planning"],"videos":[],"references":[{"position":1,"raw":"[Albrecht and Stone, 2018] Stefano V Albrecht and Peter Stone. 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