title: Decentralized Bayesian search using approximate dynamic programming methods.
abstract: We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control or predict with certainty the actions of the other agents. We formulate the multiagent search-path-planning problem as a decentralized optimal control problem and introduce approximate dynamic heuristics that can be implemented in a decentralized fashion. After establishing some analytical properties of the heuristics, we present computational results for a search problem involving two agents on a 5 x 5 grid.
authors.affiliation: Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904 USA. yz4k@virginia.edu
authors.forename: Yijia
authors.initials: Y
authors.lastname: Zhao
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Patek
authors.forename: Peter A
authors.initials: PA
authors.lastname: Beling
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011382:Programming, Linear