Titlebook: Answer Set Programming; Vladimir Lifschitz Textbook 2019 Springer Nature Switzerland AG 2019 Answer set.stable model.logic programming.dec

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https://doi.org/10.1007/978-3-662-33055-5The programming constructs described below significantly extend the expressive possibilities of the language used Chaps. . and .. The first three sections are about.—functions that apply to sets. Then we show how . can be used to solve combinatorial optimizationproblems and discuss . programs with symbolic functions and classical negation.

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Mathematics of Stable Models,In Chap. . we saw how properties of stable models expressed by Theorem on Facts, Theorem on Irrelevant Formulas, and Theorem on Constraints can be used sometimes to calculate the stable models of a program without referring to the definition of a stable model directly. This chapter discusses other useful properties of stable models.

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,Rückfahrt von K?nigsberg nach Berlin,ance, to predict and to plan. In a prediction problem, the task is to determine how the current state of a dynamic system will change after executing a given sequence of actions. In a planning problem, the task is to find a sequence of actions that leads a dynamic system from a given initial state t

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https://doi.org/10.1007/978-3-663-07073-3solvers use to find stable models, about what happens “under the hood.” Section . is, in fact, the only place where the operation of answer set solvers is discussed in any detail. The algorithms implemented in . are described in Chaps. . and . of the doctoral dissertation of one of its designers (Si