---
title: "An Adaptive Computational Model for Personalized Persuasion"
authors:
  - "Yilin Kang"
  - "Ah-Hwee Tan"
  - "Chunyan Miao"
doi: ""
url: "https://ijcai.org/proceedings/2015/16"
pdf: "https://ijcai.org/proceedings/2015/0016.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "61-66"
track: "Main Track"
keywords:
---

# An Adaptive Computational Model for Personalized Persuasion

Yilin Kang, Ah-Hwee Tan, Chunyan Miao

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 61-66.

## 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.