Increasing Threshold Search for Best-Valued Agents

David Sarne, Simon Shamoun, Eli Rata

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

Abstract

This paper investigates search techniques for multi-agent settings in which the most suitable agent, according to given criteria, needs to be found. In particular, it considers the case where the searching agent incurs a cost for learning the value of an agent and the goal is to minimize the expected overall cost of search by iteratively increasing the extent of search. This kind of search is applicable to various domains, including auctions, first responders, and sensor networks. Using an innovative transformation of the extents-based sequence to a probability-based one, the optimal sequence is proved to consist of either a single search iteration or an infinite sequence of increasing search extents. This leads to a simplified characterization of the the optimal search sequence from which it can be derived. This method is also highly useful for legacy economic-search applications, where all agents are considered suitable candidates and the goal is to optimize the search process as a whole. The effectiveness of the method for both best-valued search and economic search is demonstrated numerically using a synthetic environment.

Original languageEnglish
Title of host publicationProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
PublisherAAAI press
Pages848-853
Number of pages6
ISBN (Electronic)9781577354642
StatePublished - 15 Jul 2010
Event24th AAAI Conference on Artificial Intelligence, AAAI 2010 - Atlanta, United States
Duration: 11 Jul 201015 Jul 2010

Publication series

NameProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Conference

Conference24th AAAI Conference on Artificial Intelligence, AAAI 2010
Country/TerritoryUnited States
CityAtlanta
Period11/07/1015/07/10

Bibliographical note

Publisher Copyright:
© 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

This work was supported in part by the Israeli Ministry of Industry and Trade under project RESCUE and ISF/BSF grants 1401/09 and 2008-404.

FundersFunder number
Israeli Ministry of Industry and Trade
United States-Israel Binational Science Foundation2008-404, 1401/09
Israel Science Foundation

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