Marcello Balduccini

An A-Prolog Decision Support System for the Space Shuttle

The goal of this paper is to test if a programming methodology based on the declarative language A-Prolog and the systems for computing answer sets of such programs, can be successfully applied to the development of medium size knowledge-intensive applications. We report on a successful design and development of such a system controlling some of the functions of the Space Shuttle.

Diagnostic reasoning with A-Prolog

In this paper, we suggest an architecture for a software agent which operates a physical device and is capable of making observations and of testing and repairing the devices components. We present simplified definitions of the notions of symptom, candidate diagnosis, and diagnosis which are based on the theory of action language

Answer set based design of knowledge systems

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The USA-Advisor: A Case Study in Answer Set Planning

. The definitions allow one to give a simple account of the agents behavior in which many of the agents tasks are reduced to computing stable models of logic programs.

Issues in parallel execution of non-monotonic reasoning systems

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.

Special issue on answer set programming

In this work we show how control knowledge was used to improve planning in the USA-Advisor decision support system for the Space Shuttle. The USA-Advisor is a medium size, real-world planning application for use by NASA flight controllers and contains over a dozen domain dependent and domain independent heuristics. Experimental results are presented here, illustrating how this control knowledge helps improve both the quality of plans as well as overall system performance.

Non-monotonic Reasoning on Beowulf Platforms

We explore the major issues involved in the automatic exploitation of parallelism from the execution models of logic-based non-monotonic reasoning systems. We describe orthogonal techniques to parallelize the computation of models of non-monotonic logic theories, and demonstrate the effectiveness of the proposed techniques in prototypical implementation.

CR-MODELS: an inference engine for CR-Prolog

Since its introduction, answer set programming (ASP) has been widely applied to various knowledgeintensive tasks and combinatorial search problems. ASP was found to be closely related to SAT, which has led to new methods of computing answer sets using SAT solvers and techniques adapted from SAT. While this has been the most studied relationship, the relationship of ASP to other computing paradigms, such as constraint satisfaction, quantified boolean formulas (QBF), or first-order logic (FOL) is also the subject of active research. The goal of the annual ASPOCP (acronym for ASP and Other Computing Paradigms) workshop is to facilitate the discussion about crossing the boundaries of current ASP techniques, in combination with or inspired by other computing paradigms. In 2010, the third edition of the ASPOCP workshop (ASPOCP’10) was held in Edinburgh, Scotland, as part of the International Conference on Logic Programming (ICLP) in the frame of the 5th Federated Logic Conference (FLoC). The program included one invited talk by Torsten Schaub and nine regular paper presentations. This special issue of AI Communications contains extended and carefully reviewed versions of select contributions to ASPOCP’10. The articles reflect the range of areas the ASPOCP workshop tries to touch; in particular articles contained in this volume address preferential reasoning, domain-specific heuristics, symmetry breaking, as well as grounding issues. The first article “Potassco: The Potsdam answer set solving collection” [5] is the contribution by our invited speaker and his team. It gives a thorough overview of the suite of ASP tools that are developed at the University of Potsdam. This family of ASP tools

Learning and using domain-specific heuristics in ASP solvers

Non-monotonic logic programming systems, such as the various implementations of Answer Set Programming (ASP), are frequently used to solve problems with large search spaces. In spite of the impressive improvements in implementation technology, the sheer size of realistic computations required to solve problems of interest often makes such problems inaccessible to existing sequential technology. This paper presents some preliminary results obtained in the development of solutions for execution of Answer Set Programs on parallel architectures. We identify different forms of parallelism that can be automatically exploited in a typical ASP execution, and we describe the execution models we have experimented with to take advantage of some of these. Performance results obtained on a Beowulf system are presented.

USA-Smart: Improving the Quality of Plans in Answer Set Planning

CR-Prolog is an extension of the knowledge representation language A-Prolog. The extension is built around the introduction of consistency-restoring rules (cr-rules for short), and allows an elegant formalization of events or exceptions that are unlikely, unusual, or undesired. The flexibility of the language has been extensively demonstrated in the literature, with examples that include planning and diagnostic reasoning. In this paper we present the design of an inference engine for CR-Prolog that is efficient enough to allow the practical use of the language for medium-size applications. The capabilities of the inference engine have been successfully demonstrated with experiments on an application independently developed for use by NASA.