By Patrick Cousot (auth.), Berthe Y. Choueiry, Toby Walsh (eds.)
This quantity comprises the court cases of SARA 2000, the fourth Symposium on Abstraction, Reformulations, and Approximation (SARA). The convention used to be held at Horseshoe Bay inn and convention membership, Lake LBJ, Texas, July 26– 29, 2000, simply sooner than the AAAI 2000 convention in Austin. past 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 considering 1989. This year’s symposium was once truly scheduled to ensue at Lago Vista golf equipment & lodge 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 can be the ?rst SARA for which the court cases 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 ebook of this quantity may help the self-discipline develop even extra. Abstractions, reformulations, and approximations (AR&A) have came across - plications in a number of disciplines and difficulties together with automated progr- ming, constraint pride, layout, analysis, computer studying, making plans, qu- itative reasoning, scheduling, source allocation, and theorem proving. The - pers during this quantity trap a cross-section of those program domains.
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Additional info for Abstraction, Reformulation, and Approximation: 4th International Symposium, SARA 2000 Horseshoe Bay, USA, July 26–29, 2000 Proceedings
Park. Experience with predicate abstraction. In N. Halbwachs and D. Peled, editors, Proceedings of the Eleventh International Conference on Computer Aided Verification, CAV ’99, Trento, Italy, Lecture Notes in Computer Science 1633, pages 160–171. Springer-Verlag, Berlin, Germany, 6–10 July 1999. W. Floyd. Assigning meaning to programs. T. Schwartz, editor, Proceedings of the Symposium in Applied Mathematics, volume 19, pages 19–32. American Mathematical Society, Providence, Rhode Island, United States, 1967.
Hchoosing y = xI In particular for all X ∈ L: ¬X ⊆ γ =⇒ ¬γ H =⇒ ◦ ◦ α(¬X) ⊆ X γ = pre[h] = ¬ ◦ pre[h] ◦ ¬ = ¬ ◦ γ ¬◦¬◦γ H =⇒ γ ◦ α(¬X) ◦ Hby contraposition in LI ◦ ¬I ¬ ◦ α ◦ ¬(X) ⊆ X ¬ ◦ α ◦ ¬ = ¬ ◦ post [h] ◦ ¬ = post [h] = α and ¬ ◦ ¬(Y ) = Y I α(X) ⊆ X . Algorithm 8 X := α(I); Y := α(S); Go := (X while Go do Y ); X := α(I) α ◦ F ◦ γ(X); Y := α(S) α ◦ F Go := (X = X ) & (Y = Y ) & (X Y ); X := X ; Y := Y ; od; return if X Y then true else I don’t know; ◦ γ(Y ); Theorem 26. Under Hyps. 1, 4 & 6, Alg.
3. A task hierarchy for the Taxi domain. does ensure that each policy is locally the optimal solution to an MDP deﬁned by the subtask and the policies of all of its descendants. To learn such policies, we can apply Semi-Markov Q learning simultaneously to each task within the task hierarchy, an algorithm which we will refer to as Hierarchical Semi-Markov Q Learning (or HSMQ). Each subtask p will learn its own Q function Q(p, s, a) which is the expected total reward of performing subtask p starting in state s, executing action a and then following the optimal policy thereafter.
Abstraction, Reformulation, and Approximation: 4th International Symposium, SARA 2000 Horseshoe Bay, USA, July 26–29, 2000 Proceedings by Patrick Cousot (auth.), Berthe Y. Choueiry, Toby Walsh (eds.)