Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2015

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.

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31

Volume

2015-January

First Page

61

Last Page

67

ISBN

9781577357384

Publisher

AAAI

City or Country

Buenos Aires, Argentina

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