An informational search for a moving target

Eugene Kagan, Irad Ben-Gal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI
Pages153-155
Number of pages3
DOIs
StatePublished - 2006
Event2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI - Eilat, Israel
Duration: 15 Nov 200617 Nov 2006

Publication series

NameIEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings

Conference

Conference2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI
Country/TerritoryIsrael
CityEilat
Period15/11/0617/11/06

Keywords

  • Group testing
  • Information- theoretical data mining
  • Ishida-Korf model
  • Moving target search

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