Contextualised Value Model

Abstract

The promotion of desirable behaviours, such as socially appropriate or health-promoting actions, can be bolstered through a deeper understanding and awareness of the values that underpin the associated behavioural choices. Various implementations for promoting behaviour change based on goals already exist in Human-Robot Interaction, but, since values are the building blocks for our behaviour an agent can use values for behaviour change. By reflecting on value-related choices in a conversation, a conversational agent can assist in identifying the values at stake. This is particularly significant as these agents serve as accessible and non-judgemental platforms for discussing various concerns in a private setting. Such reflective conversations are time-consuming and may span multiple sessions, necessitating some form of memory (e.g., to reflect on earlier statements and compare the choices made for different situations).

While various robotic agents have been developed to provide behavioural support (e.g., for human health), the absence of a comprehensive memory structure and dialogue strategies capable of fostering personalised, reflective conversations based on the appreciation of certain values and actions in various scenarios through contextualised values remains a challenge. To address this, this study introduces the Contextualised Value Model – a dynamic memory model designed to facilitate value-based reflection and support personalised interactions between humans and robotic agents.

To realise this robotic memory, a conversational agent was designed that could elicit values from participants by discussing various scenarios that happen in daily life and reflecting on said values using perspective-taking and other dialogue strategies.

The evaluation of the Contextualised Value Model focused on three primary aspects: the model's accuracy, the influence on likeability and intelligence, and the effect on participants' value awareness. The model was evaluated during a between-subjects experiment (N=54), consisting of two conditions, one where the robot was able to update and use the Contextualised Value Model, and another one where the Contextualised Value Model was random throughout the conversation.

The outcome measures indicated that the integration of the memory model in conversations led to a personalised and relevant conversation, highlighting the potential of the Contextualised Value Model in enhancing conversation personalisation. Although participants' value awareness and perception of the robot's likeability and intelligence did not significantly differ based on the memory model, the study emphasised the need for extended observation to thoroughly evaluate long-term impacts.

Overall, the Contextualised Value Model presents a promising framework for enhancing personalised interactions in various real-world applications, emphasising the need for further research in this area. The ePartner4all project could be further developed to complement the efforts of primary school teachers and parents in supporting children's self-learning of socially, mentally, and physically desirable behaviours.