AIFeb 27, 2018

Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare

arXiv:1802.10054v111 citations
Originality Synthesis-oriented
AI Analysis

This work addresses behavior change in healthcare by providing a structured domain model for computational persuasion, though it is incremental as it builds on existing argument-centric approaches.

The paper tackles the problem of behavior change in healthcare by proposing a multi-dimensional domain modeling approach centered on an ontology to represent key beliefs and arguments, facilitating the acquisition and representation of arguments for use in persuasion dialogues.

The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e.g., through questionnaires or game playing) or to remember to follow a behaviour change plan (e.g., diaries and email reminders). However, recent developments in computational persuasion are leading to an argument-centric approach to persuasion that can potentially be harnessed in behaviour change applications. In this paper, we review developments in computational persuasion, and then focus on domain modelling as a key component. We present a multi-dimensional approach to domain modelling. At the core of this proposal is an ontology which provides a representation of key factors, in particular kinds of belief, which we have identified in the behaviour change literature as being important in diverse behaviour change initiatives. Our proposal for domain modelling is intended to facilitate the acquisition and representation of the arguments that can be used in persuasion dialogues, together with meta-level information about them which can be used by the persuader to make strategic choices of argument to present.

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