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Bayesian Graph Traversal
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DOI：10.1287/deca.2024.0239.png)
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Abstract
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This research considers Bayesian decision-analytic approaches toward the traversal of an uncertain graph. Namely, a traveler progresses over a graph in which rewards are gained upon a node's first visit, and costs are incurred for every edge traversal. The traveler knows the graph's adjacency matrix and the starting position but does not know the rewards and costs. The traveler is a Bayesian who encodes his beliefs about these values using a Gaussian process prior and who seeks to maximize his expected utility over these beliefs. Adopting a decision-analytic perspective, we develop sequential decision-making solution strategies for this coupled information-collection and network-routing problem. We show that the problem is NP-hard and derive properties of the optimal walk. These properties provide heuristics for the traveler's problem that balance exploration and exploitation. We provide a practical case study focused on the use of unmanned aerial systems for public safety and empirically study policy performance in myriad Erdos-Renyi settings.
Keywords:
decision analysis
online learning
sequential decision theory
stochastic optimization
longest path problem
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PROCEEDINGS OF THE IEEEIF25.9
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