{"type":"ScholarlyArticle","title":"A Goal-Directed Dialogue System for Assistance in Safety-Critical Application","authors":[{"name":"Prakash Jamakatel","slug":"prakash-jamakatel","given":"Prakash","family":"Jamakatel","affiliation":"University of the Bundeswehr Munich , Germany"},{"name":"Rebecca De Venezia","slug":"rebecca-de-venezia","given":"Rebecca","family":"De Venezia","affiliation":"Queen's University , Canada"},{"name":"Christian Muise","slug":"christian-muise","given":"Christian","family":"Muise","affiliation":"Queen's University , Canada"},{"name":"Jane Jean Kiam","slug":"jane-jean-kiam","given":"Jane Jean","family":"Kiam","affiliation":"University of the Bundeswehr Munich , Germany"}],"abstract":"In safety-critical applications where a human is in the loop, providing timely contextual assistance can reduce the severity of emergencies. While the context can typically be inferred passively, engaging the human in an active conversation with the assistance system makes this context richer and more sound. For this, we explore a FOND-planning-powered goal-directed dialogue system with Natural Language Understanding (NLU) capabilities. We use an Ultralight (UL) aviation domain as an example application for test and validation by inferring the current context in situations requiring emergency landings using the goal-directed dialogue system. The inferred context is then used for real-time modelling of the problem instance, necessary for generating strategic plans to guide the human out of the emergency situations. To overcome data scarcity, we augment the data collected from human pilots using generative text models to train the NLU capabilities of the dialogue agent. We benchmark against generative chatbots and demonstrate that our goal-directed dialogue system significantly outperforms them in context inference.","doi":"10.24963/ijcai.2024/870","url":"https://ijcai.org/proceedings/2024/870","pdf":"https://ijcai.org/proceedings/2024/0870.pdf","markdown":"https://ijcai.org/proceedings/2024/870.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/870","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":"Human-Centred AI","track":"Human-Centred AI","number":870,"pages":{"first":7859,"last":7867},"keywords":["Multidisciplinary Topics and Applications: MTA: Other","Humans and AI: HAI: Human-AI collaboration","Planning and Scheduling: PS: Applications","Natural Language Processing: NLP: Applications"],"videos":[],"references":[{"position":1,"raw":"[Abdulhak et al., 2024] Sinan Abdulhak, Wayne Hubbard, Karthik Gopalakrishnan, and Max Z. 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