Abdulrahman Amal, Richards Deborah, Bilgin Ayse Aysin
School of Computing, Macquarie University, Balaclava Road, Sydney, 2109 NSW Australia.
School of Mathematical and Physical Sciences, Macquarie University, Balaclava Road, Sydney, 2109 NSW Australia.
Auton Agent Multi Agent Syst. 2022;36(1):25. doi: 10.1007/s10458-022-09553-x. Epub 2022 Apr 4.
Virtual advisors (VAs) are being utilised almost in every service nowadays from entertainment to healthcare. To increase the user's trust in these VAs and encourage the users to follow their advice, they should have the capability of explaining their decisions, particularly, when the decision is vital such as health advice. However, the role of an explainable VA in health behaviour change is understudied. There is evidence that people tend to change their intentions towards health behaviour when the persuasion message is linked to their mental state. Thus, this study explores this link by introducing an explainable VA that provides explanation according to the user's mental state (beliefs and goals) rather than the agent's mental state as commonly utilised in explainable agents. It further explores the influence of different explanation patterns that refer to beliefs, goals, or beliefs&goals on the user's behaviour change. An explainable VA was designed to advise undergraduate students how to manage their study-related stress by motivating them to change certain behaviours. With 91 participants, the VA was evaluated and the results revealed that user-specific explanation could significantly encourage behaviour change intentions and build good user-agent relationship. Small differences were found between the three types of explanation patterns.
如今，虚拟顾问（VAs）几乎在从娱乐到医疗保健的每项服务中都有应用。为了增强用户对这些虚拟顾问的信任并鼓励用户听从它们的建议，它们应该具备解释自己决策的能力，尤其是当决策至关重要时，比如健康建议。然而，可解释的虚拟顾问在健康行为改变方面的作用尚未得到充分研究。有证据表明，当说服性信息与他们的心理状态相关联时，人们往往会改变他们对健康行为的意图。因此，本研究通过引入一种可解释的虚拟顾问来探索这种联系，该虚拟顾问根据用户的心理状态（信念和目标）提供解释，而不是像可解释智能体中通常使用的那样根据智能体的心理状态提供解释。它还进一步探讨了不同的解释模式（涉及信念、目标或信念与目标）对用户行为改变的影响。设计了一个可解释的虚拟顾问，通过激励本科生改变某些行为来指导他们如何应对与学习相关的压力。对91名参与者进行了评估，结果显示特定于用户的解释可以显著鼓励行为改变意图并建立良好的用户与智能体关系。在三种解释模式之间发现了细微差异。
