{"type":"ScholarlyArticle","title":"Artificial Agents Inspired by Human Motivation Psychology for Teamwork in Hazardous Environments","authors":[{"name":"Anupama Arukgoda","slug":"anupama-arukgoda","given":"Anupama","family":"Arukgoda","affiliation":"University of New South Wales , Canberra , Australia"},{"name":"Erandi Lakshika","slug":"erandi-lakshika","given":"Erandi","family":"Lakshika","affiliation":"University of New South Wales , Canberra , Australia"},{"name":"Michael Barlow","slug":"michael-barlow","given":"Michael","family":"Barlow","affiliation":"University of New South Wales , Canberra , Australia"},{"name":"Kasun Gunawardana","slug":"kasun-gunawardana","given":"Kasun","family":"Gunawardana","affiliation":"University of Colombo School of Computing , Colombo , Sri Lanka"}],"abstract":"Multi-agent literature explores personifying artificial agents with personality, emotions or cognitive biases to produce “typical”, believable agents. In\r\nthis study, we demonstrate the potential of endowing artificial agents with a motivation, using human implicit motivation psychology theory that introduces 3 motive profiles - power, achievement and affiliation, to create diverse, risk-aware agents. We first devise a framework to model these motivated agents (or agents with any inherent behavior), that can activate different strategies depending on the circumstances. We conduct experiments on a fire-fighting task domain, evaluate how motivated teams perform, and draw conclusions on appropriate team compositions to be deployed in environments with different risk levels. Our framework generates predictable agents as their resulting behaviors align with the inherent characteristics of their motives. We find that motivational diversity within teams is beneficial in dynamic collaborative environments, especially as the task risk level increases. Furthermore, we observed that the best composition in terms of the performance metrics used to evaluate team compositions, does not remain the same as the collaboration level required to achieve goals changes. These results have implications for future designs of risk-aware autonomous teams and Human-AI teams, as they highlight the prospects of creating better artificial teammates and performance gains that could be achieved through anthropomorphized motivated agents.","doi":"10.24963/ijcai.2023/2","url":"https://ijcai.org/proceedings/2023/2","pdf":"https://ijcai.org/proceedings/2023/0002.pdf","markdown":"https://ijcai.org/proceedings/2023/2.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/2","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":"Agent-based and Multi-agent Systems","track":"Main Track","number":2,"pages":{"first":12,"last":19},"keywords":["Humans and AI: HAI: Human-AI collaboration","Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence","Humans and AI: HAI: Cognitive modeling"],"videos":[],"references":[{"position":1,"raw":"[Azaria, 2022] Amos Azaria. 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