Learning Depth-First Search: A Unified Approach to Heuristic Search in Deterministic and Non-Deterministic Settings, and its application to MDPs

Blai Bonet, Hector Geffner

Dynamic Programming provides a convenient and unified framework for studying many state models used in AI but no algorithms for handling large spaces. Heuristic-search methods, on the other hand, can handle large spaces but lack a common foundation. In this work, we combine the benefits of a general dynamic programming formulation with the power of heuristic-search techniques for developing an algorithmic framework, that we call Learning Depth-First Search, that aims to be both general and effective. LDFS is a simple piece of code that performs iterated depth-first searches enhanced with learning. For deterministic actions and monotone value functions, LDFS reduces to IDA* with transposition tables, while for Game Trees, to the state-of-the-art iterated Alpha-Beta search algorithm with Null Windows known as MTD. For other models, like AND/OR graphs and MDPs, LDFS yields new, simple, and competitive algorithms. We show this here for MDPs.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.