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Dynamic Restart Policies

Dynamic Restart Policies. JIN Xiaolong. (Based on [1] ). Traditional Restart. Two assumptions: Only one feasible observation: the length of a run; Complete or no knowledge; Results: For complete knowledge: a fixed cutoff For no knowledge: 1, 1, 2, 1, 1, 2, 4,…. Dynamic Restart.

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Dynamic Restart Policies

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  1. Dynamic Restart Policies JIN Xiaolong (Based on [1] )

  2. Traditional Restart • Two assumptions: • Only one feasible observation: the length of a run; • Complete or no knowledge; • Results: • For complete knowledge: a fixed cutoff • For no knowledge: 1, 1, 2, 1, 1, 2, 4,…

  3. Dynamic Restart • Basic Idea: • Sample from an instance ensemble; • Explore the possible run-time distribution(s) of the samples by a Bayesian Learning Method; • Build a predictive model to predict run-time;

  4. Dynamic Restart (Cont.) One possible distribution: Multiple possible distributions: T: the cutoff; q(t): the probability of a run stopping at t; E(T): the expected run-time given a cutoff T;

  5. Results & Features • Dynamic restart policies VS Fixed restart policies: about 40~65% improvement in solution time; • Features: • Dynamically tune the cutoff; • The running is separated with the learning; • Bayesian learning method is slow in speed;

  6. References [1]. H. Kautz, E. Horvitz, Y. Ruan, C. Gomes, B. Selman.: Dynamic Restart Policies, Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, Alberta, July 2002. AAAI Press.

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