Extending and Implementing RASP

In previous work we have proposed an extension to ASP (Answer Set Programming), called RASP, standing for ASP with Resources. RASP supports declarative reasoning on production and consumption of (amounts of) resources. The approach combines answer set semantics with quantitative reasoning and relies on an algebraic structure to support computations and comparisons of amounts. The RASP framework provides some form of preference reasoning on resources usage. In this paper, we go further in this direction by introducing expressive constructs for supporting complex preferences specification on aggregate resources. We present a refinement of the semantics of RASP so as to take into account the new constructs. For all the extensions, we provide an encoding into plain ASP. We prove that the complexity of establishing the existence of an answer set, in such an enriched framework, remains NP-complete as in ASP. Finally, we report on raspberry, a prototypical implementation of RASP. This tool consists of a compiler that, given a ground RASP program, produces a pure ASP encoding suitable to be processed by commonly available ASP-solvers.

[1]  Gerd Wagner Handling inconsistency in knowledge systems , 1997 .

[2]  Arthur B. Markman,et al.  Knowledge Representation , 1998 .

[3]  Miroslaw Truszczynski,et al.  Preferences and Nonmonotonic Reasoning , 2008, AI Mag..

[4]  Martin Gebser,et al.  GrinGo : A New Grounder for Answer Set Programming , 2007, LPNMR.

[5]  Nicola Leone Logic Programming and Nonmonotonic Reasoning: From Theory to Systems and Applications , 2007, LPNMR.

[6]  Gerald Pfeifer,et al.  Design and implementation of aggregate functions in the DLV system* , 2008, Theory and Practice of Logic Programming.

[7]  Gerhard Brewka,et al.  Complex Preferences for Answer Set Optimization , 2004, KR.

[8]  Peter J. Stuckey,et al.  Semantics of Logic Programs with Aggregates , 1991, ISLP.

[9]  Stefania Costantini,et al.  Answer Set Programming with Resources , 2010, J. Log. Comput..

[10]  Shirley Dex,et al.  JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .

[11]  Danny De Schreye,et al.  Answer Set Planning , 1999 .

[12]  Enrico Pontelli,et al.  A Constructive semantic characterization of aggregates in answer set programming , 2007, Theory Pract. Log. Program..

[13]  F. Young Biochemistry , 1955, The Indian Medical Gazette.

[14]  Henry Prakken,et al.  Argument-Based Extended Logic Programming with Defeasible Priorities , 1997, J. Appl. Non Class. Logics.

[15]  Stefania Costantini,et al.  Modeling preferences and conditional preferences on resource consumption and production in ASP , 2009, J. Algorithms.

[16]  Alessandro Dal Palù,et al.  GASP: Answer Set Programming with Lazy Grounding , 2009, Fundam. Informaticae.

[17]  Samuel M. Brasil,et al.  Rules and Principles in Legal Reasoning. A Study of Vagueness and Collisions in Artificial Intelligence and Law , 2001 .

[18]  Peter Szolovits,et al.  What Is a Knowledge Representation? , 1993, AI Mag..

[19]  Michael Gelfond,et al.  Answer Sets , 2008, Handbook of Knowledge Representation.

[20]  Miroslaw Truszczynski Logic Programming for Knowledge Representation , 2007, ICLP.

[21]  Chitta Baral,et al.  Knowledge Representation, Reasoning and Declarative Problem Solving , 2003 .

[22]  Axel Polleres,et al.  Towards Logic Programs with Ordered and Unordered Disjunction ? , 2008 .

[23]  Ilkka Niemelä,et al.  Logic Programs with Ordered Disjunction , 2004, Comput. Intell..

[24]  N M Luscombe,et al.  What is Bioinformatics? A Proposed Definition and Overview of the Field , 2001, Methods of Information in Medicine.

[25]  Vladimir Lifschitz,et al.  Splitting a Logic Program , 1994, ICLP.