Depth-limited search for real-time problem solving


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.

DOI: 10.1007/BF01840464

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@article{Korf1990DepthlimitedSF, title={Depth-limited search for real-time problem solving}, author={Richard E. Korf}, journal={Real-Time Systems}, year={1990}, volume={2}, pages={7-24} }