Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating Habits - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating Habits

Résumé

In many applications, the desire is to change the behavior of users over a period of repeated episodes of similar decision making. For example, in a nutritionist scenario, one may try to encourage the user to adopt better food consumption habits. In this paper, we propose to model this recommendation scenario with a long-term goal, that we call coaching, as an iterative two-player game. At each decision time, the user makes a proposal (e.g., a meal) based on her/his preferences, the coach can then suggest a change in this proposal that the user may or may not accept. After each episode, the user updates her/his preferences and the coach adapts its model of the user. We propose a formalization of the coaching problem and discuss several possible criteria to measure the performance of a coach. Different coaching strategies are described in the paper. They are then tested and compared using a real-world dataset in the field of nutrition, where a user is simulated using a simple, but general, model of decision making and adaptation. Results show that it pays to adapt to user characteristics and use non-myopic strategies, which aim for long-term gains, when the number of interactions becomes large. Although illustrated on choice sequences for food consumption, the scope of the proposed method goes far beyond this use case, as in sports, health, entertainment or tourist activity choices, etc.
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Dates et versions

hal-04015804 , version 1 (17-01-2024)

Identifiants

  • HAL Id : hal-04015804 , version 1

Citer

Jules Vandeputte, Antoine Cornuéjols, Nicolas N. Darcel, Fabien Delaere, Christine Martin. Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating Habits. 21st International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, May 2022, Online, France. pp.1292-1300. ⟨hal-04015804⟩
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