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An early example of answer set programming was the planning method proposed in 1997 by Dimopoulos, Nebel and Köhler. [3] [4] Their approach is based on the relationship between plans and stable models. [5] In 1998 Soininen and Niemelä [6] applied what is now known as answer set programming to the problem of product configuration. [4]
The concept of a stable model, or answer set, is used to define a declarative semantics for logic programs with negation as failure. This is one of several standard approaches to the meaning of negation in logic programming, along with program completion and the well-founded semantics. The stable model semantics is the basis of answer set ...
Datalog is a syntactic subset of Prolog, disjunctive Datalog, answer set programming, DatalogZ, and constraint logic programming. When evaluated as an answer set program, a Datalog program yields a single answer set, which is exactly its minimal model. [26]
A logic program is a set of sentences in logical form, representing knowledge about some problem domain. Computation is performed by applying logical reasoning to that knowledge, to solve problems in the domain. Major logic programming language families include Prolog, Answer Set Programming (ASP) and Datalog.
The probabilistic logic programming language P-Log resolves this by dividing the probability mass equally between the answer sets, following the principle of indifference. [4] [6] Alternatively, probabilistic answer set programming under the credal semantics allocates a credal set to every query. Its lower probability bound is defined by only ...
So, the goal is to select a well-performing SAT solver for each individual instance. In the same way, algorithm selection can be applied to many other -hard problems (such as mixed integer programming, CSP, AI planning, TSP, MAXSAT, QBF and answer set programming).
Vladimir Lifschitz (born 30 May 1947) is the Gottesman Family Centennial Professor in Computer Sciences at the University of Texas at Austin.He received a degree in mathematics from the Steklov Institute of Mathematics in Russia in 1971 and emigrated to the United States in 1976.
By comparison, answer set programming is also based on predicates (more precisely, on atomic sentences created from atomic formulas). Unlike SMT, answer-set programs do not have quantifiers, and cannot easily express constraints such as linear arithmetic or difference logic —answer set programming is best suited to Boolean problems that ...