{"type":"ScholarlyArticle","title":"An Adaptive Computational Model for Personalized Persuasion","authors":[{"name":"Yilin Kang","slug":"yilin-kang","given":"Yilin","family":"Kang","affiliation":""},{"name":"Ah-Hwee Tan","slug":"ah-hwee-tan","given":"Ah-Hwee","family":"Tan","affiliation":""},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":""}],"abstract":"While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which can provide a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals' personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse agent who takes care and recommends healthy lifestyle habits to the elderly. Our experimental results show that the MAP-based agent is able to change the others' attitudes and behaviors intentionally, interpret individual differences between users, and adapt to user's behavior for effective persuasion.","doi":null,"url":"https://ijcai.org/proceedings/2015/16","pdf":"https://ijcai.org/proceedings/2015/0016.pdf","markdown":"https://ijcai.org/proceedings/2015/16.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/16","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Agent and Multiagent Systems","track":"Main Track","number":16,"pages":{"first":61,"last":66},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}