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Depth-limited search for real-time problem solving

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Abstract

We propose depth-limited heuristic search as a general paradigm for real-time problem solving in a dynamic environment. When combined with iterative-deepening, it provides the ability to commit to an action almost instantaneously, but allows the quality of that decision to improve as long as time is available. Once a deadline is reached, the best decision arrived at is executed. We illustrate the paradigm in three different settings, corresponding to single-agent search, two-player games, and multi-agent problem solving. First we review two-player minimax search with alpha-beta pruning. Minimax can be extended to themaxn algorithm for more than two players, which admits a much weaker form of alpha-beta pruning. Finally, we explore real-time search algorithms for single-agent problems. Minimax is specialized tominimin, which allows a very powerfulalpha pruning algorithm. In addition,real-time-A * allows backtracking while still guaranteeing a solution and making locally optimal decisions.

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Authors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, 90024, Los Angeles, CA, USA

    Richard E. Korf

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  1. Richard E. Korf
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Additional information

This research was supported by an NSF Presidential Young Investigator Award, NSF Grant IRI-8801939, and and equipment grant from Hewlett-Packard. Thanks to Valerie Aylett for drawing the figures.

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Korf, R.E. Depth-limited search for real-time problem solving. Real-Time Syst 2, 7–24 (1990). https://doi.org/10.1007/BF01840464

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