Autonomous Nondeterministic Tour Guides: Improving Quality of Experience with TTD-MDPs
Cantino, Andrew S.
Roberts, David L.
Isbell, Charles Lee, Jr.
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In this paper, we address the problem of building a system of autonomous tour guides for a complex environment, such as a museum with many visitors. Visitors may have varying preferences for types of art or may wish to visit different areas across multiple visits. Often, these goals conflict. For example, many visitors may wish to see the museum's most popular work, but that could cause congestion, ruining the experience. Thus, our task is to build a set of agents that can satisfy their visitors' goals while simultaneously providing quality experiences for all. We use targeted trajectory distribution MDPs (TTD-MDPs), a technology developed to guide players in an interactive entertainment setting. The solution to a TTD-MDP is a probabilistic policy that results in a specific distribution of trajectories through a state space. We motivate TTD-MDPs for the museum tour problem, then describe the development of a number of models of museum visitors. Additionally, we propose a museum model and simulate tours using personalized TTD-MDP tour guides for each kind of visitor. We explain how the use of probabilistic policies reduces the congestion experienced by visitors while preserving their ability to pursue and realize goals.