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An adaptive agent dialogue framework for driving sustainable dietary behaviour change

   UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents

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  Mr Jared de Bruin  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.


  • Mathieu Chollet:  School of Computing Science
  • Esther Papies: School of Psychology

The food system contributes 34% of greenhouse gas emissions, the majority of which coming from animal agriculture [1] also disproportionately contributing to deforestation, water scarcity, biodiversity loss, and ecosystem pollution [2]. Despite this, most consumers are resistant to substantially reduce their meat consumption, even when considering the accompanying health benefits. Efforts to improve eating habits are traditionally approached through behaviour change counselling sessions with dieticians. Such approaches are time and resource consuming, but digital intervention alternatives lack the essential component of human interaction and social support that drives the effectiveness of behaviour change counselling [3]. Virtual agents hold the potential to fill that gap; however past approaches have typically only been loosely coupled to existing social science in behaviour change [4].

Objectives and novelty

The project will focus on designing a virtual agent dialogue framework for longitudinal behaviour change interactions rooted in an established psychological theory. The adaptive dialogue agent will be able to guide users through their journey towards dietary change, interspersing activities from behaviour change programmes with social dialogue aimed at reinforcing the user-agent relationship while simultaneously probing users’ preferences and attitudes. These preference-infering exchanges will help maintain and update user models including idiosyncratic sensitivities to key variables identified to be key drivers for transitioning to more plant-based foods [5]: Taste expectations (i.e. meat-based foods are expected to be tastier), Availability (i.e. plant-based foods are less widely available in many settings), Skills (many consumers don’t know how to prepare meat-free meals), Identity (Vegetarian/vegan social identities are not seen as positive by many consumers and contribute to the polarization of perspectives on sustainable eating) and Social Norms (consuming meat is seen as normative, and these norms are communicated through features of the food environment and others’ behaviour). These user models will further impact task-related and relationship-building tasks, altering dialogue such as agents’ food presentation strategies. A key research challenge will consist in designing dialogue policies reconciling concurrent but inter-linked dialogue goals, in this case preference-infering, relationship-building, and delivering task-related dialogue.

Methods & Timeline

After a literature review, the student will extend an existing socially-aware recipe recommender agent framework developed at UofG [6] with a baseline rule-based dialogue model for inferring user preferences and attitudes and integrating these variables to alter subsequent dialogue. The model will be used to collect initial data and train further model iterations, considering supervised/reinforcement learning approaches. The resulting dialogue models will be deployed in a series of user experiments to evaluate their effectiveness at promoting user engagement and motivation, infering accurate user models, and driving effective and long-lasting behaviour change.

Outputs and impact

The project is expected to contribute novel dialogue models and policies for human-agent interactions as well as methodological and experimental insights on technologically-mediated behaviour change frameworks. The project’s findings may further feedback into theory formation on habit change and maintenance. The project will have societal impact, both locally through deployments of the resulting behaviour change framework, and further through dissemination with academic and institutional partners.


[1] Xu, X., Sharma, P., Shu, S., Lin, T.-S., Ciais, P., Tubiello, F. N., Smith, P., Campbell, N., & Jain, A. K. (2021). Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nature Food, 1–9.
[2] Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987–992.[4] Graça, J., Godinho, C. A., & Truninger, M. (2019). Reducing meat consumption and following plant-based diets: Current evidence and future directions to inform integrated transitions. Trends in Food Science & Technology, 91, 380–390.
[3] Schippers, M., et al. “A meta‐analysis of overall effects of weight loss interventions delivered via mobile phones and effect size differences according to delivery mode, personal contact, and intervention intensity and duration.” Obesity reviews 18.4 (2017): 450-459.
[4] Bickmore, Timothy W., et al. “A randomized controlled trial of an automated exercise coach for older adults.” Journal of the American Geriatrics Society 61.10 (2013): 1676-1683.
[5] Papies, E. K., Johannes, N., Daneva, T., Semyte, G., & Kauhanen, L.-L. (2020). Using consumption and reward simulations to increase the appeal of plant-based foods. Appetite, 155, 104812.
[6] Florian Pecune, Lucile Callebert, and Stacy Marsella. 2020. A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations. In Proceedings of the 8th International Conference on Human-Agent Interaction (HAI ’20). Association for Computing Machinery, New York, NY, USA, 78–86. DOI:
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