In this paper we give a short introduction to logic programming approach to knowledge representation and reasoning. The intention is to help the reader to develop a 'feel' for the field's history and some of its recent developments. The discussion is mainly limited to logic programs under the answer set semantics. For understanding of approaches to logic programming build on well-founded semantics, general theories of argumentation, abductive reasoning, etc., the reader is referred to other publications.

In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of A-Prolog, can be used to represent various types of knowledge about the world. By way of example we demonstrate how the corresponding representations together with inference mechanisms associated with A-Prolog can be used to solve various programming tasks.

We present an extension of language A-Prolog by consistency-restoring rules with preferences, give the semantics of the new language, CR-Prolog, and show how the language can be used to formalize various types of commonsense knowledge and reasoning.

Abstract. This paper discusses the background, algorithms and implementation techniques to support programmers in ‘debugging ’ logic programs under the answer set semantics. We first investigate what constitutes an error in such programs and which classes of errors exist. This is used to motivate techniques and algorithms that respectively eliminate certain classes of errors and explain how an error occurred and why it occurred. Finally, details of the IDEAS (Interactive Development and Evaluation tool for Answer Set Semantics) system are given, a prototype version of which implements all of the techniques described. 1

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The aim of this paper is to demonstrate that A-Prolog is a powerful language for the construction of reasoning systems. In fact, A-Prolog allows to specify the initial situation, the domain model, the control knowledge, and the reasoning modules. Moreover, it is efficient enough to be used for practical tasks and can be nicely integrated with programming languages such as Java. An extension of A-Prolog (CR-Prolog) allows to further improve the quality of reasoning by specifying requirements that the solutions should satisfy if at all possible. The features of A-Prolog and CR-Prolog are demonstrated by describing in detail the design of USA-Advisor, an A-Prolog based decision support system for the Space Shuttle flight controllers.

Answer set programming (ASP) is a form of declarative programming oriented towards difficult search problems. As an outgrowth of research on the use of nonmonotonic reasoning in knowledge representation, it is particularly useful in knowledge-intensive applications. ASP programs consist of rules that look like Prolog rules, but the computational mechanisms used in ASP are different: they are based on the ideas that have led to the creation of fast satisfiability solvers for propositional logic.

The work aims at extending Answer Set Programming (ASP) with the possibility of quickly introducing new predefined constructs and to deal with compound data structures. We show how ASP can be extended with `template' predicate's definitions by introducing a wellsuited form of second order logics. We present language syntax and give its operational semantics. We show that the theory supporting our ASP extension is sound, and that program encodings are evaluated as e#ciently as ASP programs. Examples show how the extended language increases declarativity, readability, compactness of program encodings and code reusability.

In this paper we show how CR-Prolog, a recent extension of A-Prolog, was used in the successor of USA-Advisor (USA-Smart) in order to improve the quality of the plans returned. The general problem that we address is that of improving the quality of plans by taking in consideration statements that describe "most desirable" plans. We believe that USA-Smart proves that CR-Prolog provides a simple, elegant, and flexible solution to this problem, and can be easily applied to any planning domain. We also discuss how alternative extensions of A-Prolog can be used to obtain similar results.

Abstract. We present a new approach to integrating Constraint Processing (CP) techniques into Answer Set Programming (ASP). Based on an alternative semantic approach, we develop an algorithmic framework for conflict-driven ASP solving that exploits CP solving capacities. A significant technical issue concerns the combination of conflict information from different solver types. We have implemented our approach, combining ASP solver clingo with the generic CP solver gecode, and we empirically investigate its computational impact. 1