Giving advice to people in path selection problems

We present a novel computational method for advice-generation in path selection problems which are difficult for people to solve. The advisor agent's interests may conflict with the interests of the people who receive the advice. Such optimization settings arise in many human-computer applications in which agents and people are self-interested but also share certain goals, such as automatic route-selection systems that also reason about environmental costs. This paper presents an agent that clusters people into one of several types, based on how their path selection behavior adheres to the paths preferred by the agent and are not necessarily preferred by the people. It predicts the likelihood that people deviate from these suggested paths and uses a decision theoretic approach to suggest modified paths to people that will maximize the agent's expected benefit. This technique was evaluated empirically in an extensive study involving hundreds of human subjects solving the path selection problem in mazes. Results showed that the agent was able to outper-form alternative methods that solely considered the benefit to the agent or the person, or did not provide any advice.