Annals of Mathematics and Artificial Intelligence

, Volume 69, Issue 2, pp 183–205

Iterative-deepening search with on-line tree size prediction



The memory requirements of best-first graph search algorithms such as A* often prevent them from solving large problems. The best-known approach for coping with this issue is iterative deepening, which performs a series of bounded depth-first searches. Unfortunately, iterative deepening only performs well when successive cost bounds visit a geometrically increasing number of nodes. While it happens to work acceptably for the classic sliding tile puzzle, IDA* fails for many other domains. In this paper, we present an algorithm that adaptively chooses appropriate cost bounds on-line during search. During each iteration, it learns a model of the search tree that helps it to predict the bound to use next. Our search tree model has three main benefits over previous approaches: (1) it will work in domains with real-valued heuristic estimates, (2) it can be trained on-line, and (3) it is able to make more accurate predictions with only a small number of training examples. We demonstrate the power of our improved model by using it to control an iterative-deepening A* search on-line. While our technique has more overhead than previous methods for controlling iterative-deepening A*, it can give more robust performance by using its experience to accurately double the amount of search effort between iterations.


Heuristic search Tree search Tree size prediction Reactive search Autonomous search 

Mathematics Subject Classifications (2010)

68R05 68R10 


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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of New HampshireDurhamUnited States

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