Download Abstraction, Reformulation, and Approximation: 4th by Patrick Cousot (auth.), Berthe Y. Choueiry, Toby Walsh PDF

By Patrick Cousot (auth.), Berthe Y. Choueiry, Toby Walsh (eds.)

This quantity comprises the lawsuits of SARA 2000, the fourth Symposium on Abstraction, Reformulations, and Approximation (SARA). The convention was once held at Horseshoe Bay hotel and convention membership, Lake LBJ, Texas, July 26– 29, 2000, simply sooner than the AAAI 2000 convention in Austin. prior SARA meetings happened at Jackson gap in Wyoming (1994), Ville d’Est´erel in Qu´ebec (1995), and Asilomar in California (1998). The symposium grewout of a sequence of workshops on abstraction, approximation, and reformulation that had taken position along AAAI because 1989. This year’s symposium used to be truly scheduled to occur at Lago Vista golf equipment & hotel on Lake Travis yet, because of the resort’s failure to pay taxes, the convention needed to be moved past due within the day. This mischance engendered eleventh-hour reformulations, abstractions, and source re-allocations of its personal. Such are the perils of organizing a convention. this is often the ?rst SARA for which the lawsuits were released within the LNAI sequence of Springer-Verlag. we are hoping that this can be a re?ection of the elevated adulthood of the ?eld and that the elevated visibility introduced by way of the booklet of this quantity can assist the self-discipline develop even extra. Abstractions, reformulations, and approximations (AR&A) have discovered - plications in quite a few disciplines and difficulties together with computerized progr- ming, constraint pride, layout, analysis, computer studying, making plans, qu- itative reasoning, scheduling, source allocation, and theorem proving. The - pers during this quantity catch a cross-section of those software domains.

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Additional resources for Abstraction, Reformulation, and Approximation: 4th International Symposium, SARA 2000 Horseshoe Bay, USA, July 26–29, 2000 Proceedings

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Barto, A. G. (1995). Improving elevator performance using reinforcement learning. In Advances in Neural Information Processing Systems, Vol. 8, pp. 1017–1023 San Francisco, CA. Morgan Kaufmann. , & Hinton, G. (1993). Feudal reinforcement learning. In Advances in Neural Information Processing Systems, 5, pp. 271–278. Morgan Kaufmann, San Francisco, CA. -H. (1995). Decomposition techniques for planning in stochastic domains. Tech. rep. CS-95-10, Department of Computer Science, Brown University, Providence, Rhode Island.

Choueiry and T. ): SARA 2000, LNAI 1864, pp. 26–44, 2000. c Springer-Verlag Berlin Heidelberg 2000 An Overview of MAXQ Hierarchical Reinforcement Learning 27 The paper begins (in Section 2) with an introduction to Markov decision processes and a toy problem that will serve as the running example for the paper. It introduces the fundamental knowledge structure of most reinforcement learning algorithms—the value function. The most fundamental reinforcement learning algorithm, Q learning, is introduced as well.

2 and monotonyI Hby Hyp. 2 so that α preserves lubsI δ+1 Hby def. O : Xλ = Xβ Hby def. I α(X β ) Hind. hyp. and def. lubsI Xβ) Hby Hyp. 2 so that α preserves lubsI β<λ β<λ α( β<λ λ Hby def. I = α(X ) ≤ There exists ∈ O such that α(lfp λ X · I ∨ F (X)) = α(X ) X = lfp λ X · α(I) α ◦ F ◦ γ(X) so that by the Galois connection Hyp. 2, we ≤ conclude that lfp λ X · I ∨ F (X) ≤ γ(lfp λ X · α(I) α ◦ F ◦ γ(X)). By duality, we get: ≤ Theorem 29. Under Hyps. 1, 4 and 6, we have gfp λ X · S ∧ F (X) ≥ γ(gfp λ X · α(S) α ◦ F ◦ γ(X)).

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