Answer-set programming (ASP) is widely recognised as a viable tool for declarative problem solving. However, there is currently a lack of tools for developing answer-set programs. In particular, providing tools for debugging answer-set programs has recently been identified as a crucial prerequisite for a wider acceptance of ASP. In this paper, we introduce a meta-programming technique for debugging in ASP. The basic question we address is why interpretations expected to be answer sets are not answer sets of the program to debug. We thus deal with finding semantical errors of programs. The explanations provided by our method are based on an intuitive scheme of errors that relies on a recent characterisation of the answer-set semantics. Furthermore, as we are using a metaprogramming technique, debugging queries are expressed in terms of answer-set programs themselves, which has several benefits: For one, we can directly use ASP solvers for processing debugging queries. Indeed, our technique can easily be implemented, and we devised a corresponding prototype debugging system. Also, our approach respects the declarative nature of ASP, and the capabilities of the system can easily be extended to incorporate differing debugging features.

Abstract. Fuzzy answer set programming has been introduced as a framework that successfully combines the concepts of answer set programming and fuzzy logic. In this paper, we show how the fuzzy answer set semantics can be mapped onto the semantics for HEX-programs, which are nonmonotonic logic programs under the answer set semantics that support the use of external function calls. By using the DLVHEX reasoning engine, we so devise a vehicle for effectively computing fuzzy answer sets. 1

Multi-agent resource allocation is a growing area of research at the frontier between Economics and Computer Science. Despite the extensive theoretical work and raising number of practical applications, many fundamental problems in multi-agent resource allocation still require adequate attention to its computational aspects. This paper addresses computational aspects of multi-agent resource allocation through the use of a fully declarative and highly expressive logic programming paradigm – Answer Set Programming – to provide uniform, general and flexible solutions to many multi-agent resource allocation problems in a compact and declarative manner.

Abstract. Logic Programming paradigms that allow for expressing preferences have drawn a lot of research interest over the last years. Among them, the principle of ordered disjunction was developed to express totally ordered preferences for alternatives in rule heads. In this paper we introduce an extension of this approach called Disjunctive Logic Programs with Ordered Disjunction (DLPOD) that combines ordered disjunction with common disjunction in rule heads. By this extension, we enhance the preference notions expressible with totally ordered disjunctions to partially ordered preferences. Furthermore, we show that computing optimal stable models for DLPODs still stays in Σ p 2 and establish Σ p 3 for head-cycle free programs upper bounds for the general case. 1

Multi-agent resource allocation is a growing area of research at the frontier between Economics and Computer Science. Despite the extensive theoretical work and raising number of practical applications, many fundamental problems in multi-agent resource allocation still require adequate attention to its computational aspects. This paper addresses computational aspects of multi-agent resource allocation through the use of a fully declarative and highly expressive logic programming paradigm – Answer Set Programming – to provide uniform, general and flexible solutions to many multi-agent resource allocation problems in a compact and declarative manner.

submitted [TBA]; revised [TBA]; accepted [TBA] Preference handling and optimization are indispensable means for addressing non-trivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, like saturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-programming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications. 1