Predicting actions using an adaptive probabilistic model of human decision behaviours

Abstract: Computer interfaces provide an environment that allows for multiple objectively optimal solutions but individuals will, over time, use a smaller number of subjectively optimal solutions, developed as habits that have been formed and tuned by repetition. Designing an interface agent to provide assistance in this environment thus requires not only knowledge of the objectively optimal solutions, but also recognition that users act from habit and that adaptation to an individual’s subjectively optimal solutions is required. We present a dynamic Bayesian network model for predicting a user’s actions by inferring whether a decision is being made by deliberation or through habit. The model adapts to individuals in a principled manner by incorporating observed actions using Bayesian probabilistic techniques. We demonstrate the model’s effectiveness using specific implementations of deliberation and habitual decision making, that are simple enough to transparently expose the mechanisms of our estimation procedure. We show that this implementation achieves > 90% prediction accuracy in a task with a large number of optimal solutions and a high degree of freedom in selecting actions.

Citation: A.H. Cruickshank, R. Shillcock, S. Ramamoorthy, Predicting actions using an adaptive probabilistic model of human decision behaviours, Poster, In Ext. Proc. Conference on User Modelling, Adaptation and Personalization (UMAP), 2015.


About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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