Abstract
As a primary means for representing and reasoning about knowledge, Answer Set Programming (ASP) has been applying in many areas such as planning, decision making, fault diagnosing and increasingly prevalent e-service. Based on the stable model semantics of logic programming, ASP can be used to solve various combinatorial search problems by finding the answer sets of logic programs which declaratively describe the problems. It's not an easy task to compute answer sets of a logic program using Gelfond and Lifschitz's definition directly. In this paper, we show some results on characterization of answer sets of a logic program with constraints, and propose a way to split a program into several non-intersecting parts step by step, thus the computation of answer sets for every subprogram becomes relatively easy. To instantiate our splitting computation theory, an example about personalized product configuration in e-retailing is given to show the effectiveness of our method.
Highlights
The Internet has been reaching almost all aspects of our lives, many online services emerged as the times require, including e-government, e-business, e-learning, ecommerce, e-recruitment, and so on
As a primary means for non-monotonic reasoning, Answer Set Programming(ASP) is a paradigm based on the stable model(answer set) semantics of logic programming,[2] it is a method that reduces solving of various combinatorial search problems to finding the answer sets of logic programs which declaratively describe the problems
With the notion of compatibility and Λ-operator, we described the characterization of answer set for a logic program with constraints
Summary
The Internet has been reaching almost all aspects of our lives, many online services emerged as the times require, including e-government, e-business, e-learning, ecommerce, e-recruitment, and so on. As a primary means for non-monotonic reasoning, Answer Set Programming(ASP) is a paradigm based on the stable model(answer set) semantics of logic programming,[2] it is a method that reduces solving of various combinatorial search problems to finding the answer sets of logic programs which declaratively describe the problems. Eiter et al introduced a new declarative language K based on non-monotonic logic programming.[5] Transitions between states of knowledge can be described in K, so it is suitable for planning under incomplete knowledge. ASP provides a useful approach, but to find all answer sets of a logic program is a problem with comparative complexity. Splitting Computation of Answer Set Program and Its Application on E-service configuration according to customer’s favor, which could include incomplete information. The last section concludes our work and presents the future research interests
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