An Informational Search for a Moving Target

We consider the problem of search for a randomly moving target in a discrete domain. The action available to the searcher is checking a sub-domain to detect whether the target is somewhere in this sub-domain or not. The procedure terminates if the searcher finds the target in a sub-domain that contains only one point. Starting from the Korf and Ishida-Korf algorithms, we suggest the informational learning real-time algorithm and the informational moving target search algorithm running on a states space with informational metric. We describe the properties of these algorithms and compare them with the known Zimmerman search procedure, with the generalized optimal testing algorithm, designed by Hartmann et al, and with the Pollock model of search. To illustrate the work of the informational moving target search algorithm, we present the results of simulative trials in comparison with the greedy probabilistic search procedure.