Optimizing recursive information gathering plans

Eric Lambrecht, Subbarao Kambhampati, Senthil Gnanaprakasam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations


In this paper we describe two optimization techniques that are specially tailored for information gathering. The first is a greedy minimization algorithm that minimizes an information gathering plan by removing redundant and overlapping information sources without loss of completeness. We then discuss a set of heuristics that guide the greedy minimization algorithm so as to remove costlier information sources first. In contrast to previous work, our approach can handle recursive query plans that arise commonly in practice. Second, we present a method for ordering the access to sources to reduce the execution cost. Sources on the Internet have a variety of access limitations and the execution cost in information gathering is affected both by network traffic and by the connection setup costs. We describe a way of representing the access capabilities of sources, and provide a greedy algorithm for ordering source calls that respects source limitations, and takes both access costs and traffic costs into account, without requring full source statistics. Finally, we will discuss implementation and empirical evaluation of these methods in Emerac, our prototype information gathering system.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Number of pages7
StatePublished - 1999
Event16th International Joint Conference on Artificial Intelligence, IJCAI 1999 - Stockholm, Sweden
Duration: Jul 31 1999Aug 6 1999


Other16th International Joint Conference on Artificial Intelligence, IJCAI 1999

ASJC Scopus subject areas

  • Artificial Intelligence


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