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AAAI 1990

Iterative Broadening

Conference Paper Search Artificial Intelligence

Abstract

Conventional blind search techniques generally assume that the goal nodes for a given problem are distributed randomly along the fringe of the search tree. We argue that this is often invalid in practice, suggest that a more reasonable assumption is that decisions made at each point in the search carry equal weight, and show that a new search technique that we call iterative broadening leads to orders-of-magnitude savings in the time needed to search a space satisfying this assumption. Both theoretical and experimental results are presented.

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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
409694391225056163