An adaptive interactive agent for route advice

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Abstract
Current route advice systems present a single route to the driver based on static evaluation criteria, with little or no recourse if the driver finds this solution unsatisfactory. In this paper, we propose a more flexible approach and its im- plementation in the Adaptive Route Advisor. Our system behaves more like a human travel agent, using driver pref- erences, when known, and working with him or her to find a satisfactory route. The route advisor predicts what route a driver will prefer based on a model of driver preferences, and, if the predicted route is unsatisfactory, it generates addi- tional routes based on interaction with the driver. The route that the driver eventually selects serves as feedback to im- prove the preference model. We present a pilot study on us- ing these route selections to construct a personalized model. As the preference model becomes more accurate, the need for interaction decreases and the driver receives better route advice.

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