Recursive Bayesian Estimation Search with Environmental Constraints and Psychological Beliefs and Biases

In the paper, we consider a modification of the Recursive Bayesian Estimation technique and incorporate the Fast Sweeping Method to extend recent work in search applications with an algorithm capable of calculating optimal trajectories in the context of multiple targets and searchers. In addition to providing a computational overview of the algorithm, we demonstrate how incorporating knowledge, deception, and belief biases into the algorithm alters the optimal trajectories of the searchers. Finally, we present Monte-Carlo simulations of how these psychological factors influence the mean probability that the searchers detect the target. We will discuss the implications of the findings, current limitations and future extensions of the model, and potential applications to decision support.