Exploring Relations between Answer Set Programs

Equivalence and generality relations over logic programs have been proposed in answer set programming to semantically compare information contents of logic programs. In this paper, we overview previous relations of answer set programs, and propose a general framework that subsumes previous relations. The proposed framework allows us to compare programs possibly having non-minimal answer sets as well as to explore new relations between programs. Such new relations include relativized variants of generality relations over logic programs. By selecting contexts for comparison, the proposed framework can represent weak, strong and uniform variants of generality, inclusion and equivalence relations. These new relations can be applied to comparison of abductive logic programs and coordination of multiple answer set programs.

[1]  Dov M. Gabbay,et al.  Handbook of logic in artificial intelligence and logic programming (vol. 1) , 1993 .

[2]  Carl A. Gunter,et al.  Semantic Domains , 1991, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.

[3]  Alex S. Taylor,et al.  Machine intelligence , 2009, CHI.

[4]  Chiaki Sakama,et al.  An alternative approach to the semantics of disjunctive logic programs and deductive databases , 2004, Journal of Automated Reasoning.

[5]  Michael Gelfond,et al.  Classical negation in logic programs and disjunctive databases , 1991, New Generation Computing.

[6]  Yehoshua Sagiv,et al.  Optimizing datalog programs , 1987, Foundations of Deductive Databases and Logic Programming..

[7]  Chiaki Sakama,et al.  Equivalence in Abductive Logic , 2005, IJCAI.

[8]  Hans Tompits,et al.  Combining answer set programming with description logics for the Semantic Web , 2004, Artif. Intell..

[9]  Chiaki Sakama,et al.  Ordering default theories and nonmonotonic logic programs , 2005, Theor. Comput. Sci..

[10]  Yehoshua Sagiv Optimizing Datalog Programs , 1988, Foundations of Deductive Databases and Logic Programming..

[11]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[12]  Hans Tompits,et al.  Casting Away Disjunction and Negation under a Generalisation of Strong Equivalence with Projection , 2009, LPNMR.

[13]  Miguel A. Ballester,et al.  Extending an order to the power set: The Leximax Criterion , 2003, Soc. Choice Welf..

[14]  Chiaki Sakama,et al.  Brave induction: a logical framework for learning from incomplete information , 2009, Machine Learning.

[15]  Fangzhen Lin Reducing Strong Equivalence of Logic Programs to Entailment in Classical Propositional Logic , 2002, KR.

[16]  Shan-Hwei Nienhuys-Cheng,et al.  Foundations of Inductive Logic Programming , 1997, Lecture Notes in Computer Science.

[17]  Raymond Reiter,et al.  A Logic for Default Reasoning , 1987, Artif. Intell..

[18]  Stefan Woltran,et al.  Relativised Equivalence in Equilibrium Logic and its Applications to Prediction and Explanation: Preliminary Report , 2007, CENT.

[19]  Stefan Woltran,et al.  Merging Logic Programs under Answer Set Semantics , 2009, ICLP.

[20]  Stefan Woltran,et al.  Facts Do Not Cease to Exist Because They Are Ignored: Relativised Uniform Equivalence with Answer-Set Projection , 2007, AAAI.

[21]  Gordon Plotkin,et al.  A Note on Inductive Generalization , 2008 .

[22]  Trevor J. M. Bench-Capon,et al.  On the Instantiation of Knowledge Bases in Abstract Argumentation Frameworks , 2013, CLIMA.

[23]  Jack Minker Foundations of deductive databases and logic programming , 1988 .

[24]  Ramón P. Otero,et al.  Induction of Stable Models , 2001, ILP.

[25]  Chiaki Sakama,et al.  Prioritized logic programming and its application to commonsense reasoning , 2000, Artif. Intell..

[26]  Chiaki Sakama,et al.  Negation as Failure in the Head , 1998, J. Log. Program..

[27]  Chiaki Sakama,et al.  Combining Answer Sets of Nonmonotonic Logic Programs , 2005, CLIMA.

[28]  Paolo Ferraris,et al.  Answer Sets for Propositional Theories , 2005, LPNMR.

[29]  Chiaki Sakama,et al.  Generality and Equivalence Relations in Default Logic , 2007, AAAI.

[30]  Chiaki Sakama,et al.  Generality Relations in Answer Set Programming , 2006, ICLP.

[31]  Krzysztof R. Apt,et al.  Logic Programming , 1990, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.

[32]  Stefan Woltran,et al.  On Solution Correspondences in Answer-Set Programming , 2005, IJCAI.

[33]  Jan van Leeuwen,et al.  Handbook of Theoretical Computer Science, Vol. B: Formal Models and Semantics , 1994 .

[34]  Victor W. Marek,et al.  Logic programs with monotone abstract constraint atoms* , 2006, Theory and Practice of Logic Programming.

[35]  Chiaki Sakama,et al.  Constructing Consensus Logic Programs , 2006, LOPSTR.

[36]  Yan Zhang,et al.  Rule Calculus: Semantics, Axioms and Applications , 2008, JELIA.

[37]  Stefan Woltran,et al.  Strong and Uniform Equivalence in Answer-Set Programming: Characterizations and Complexity Results for the Non-Ground Case , 2005, AAAI.

[38]  Stefan Woltran,et al.  Characterizations for Relativized Notions of Equivalence in Answer Set Programming , 2004, JELIA.

[39]  Chiaki Sakama,et al.  Comparing Abductive Theories , 2008, ECAI.

[40]  J. Van Leeuwen,et al.  Handbook of theoretical computer science - Part A: Algorithms and complexity; Part B: Formal models and semantics , 1990 .

[41]  Chiaki Sakama,et al.  Induction from answer sets in nonmonotonic logic programs , 2005, TOCL.

[42]  Antonis C. Kakas,et al.  The role of abduction in logic programming , 1998 .

[43]  Wolfgang Faber,et al.  Logic Programming and Nonmonotonic Reasoning , 2011, Lecture Notes in Computer Science.

[44]  Vladimir Lifschitz,et al.  Answer Sets in General Nonmonotonic Reasoning (Preliminary Report) , 1992, KR.

[45]  Chiaki Sakama,et al.  Equivalence of Logic Programs Under Updates , 2004, JELIA.

[46]  Ka-Shu Wong Sound and Complete Inference Rules for SE-Consequence , 2008, J. Artif. Intell. Res..

[47]  Miroslaw Truszczynski,et al.  Hyperequivalence of logic programs with respect to supported models , 2008, Annals of Mathematics and Artificial Intelligence.

[48]  V. S. Costa,et al.  Theory and Practice of Logic Programming , 2010 .

[49]  David Pearce,et al.  A Characterization of Strong Equivalence for Logic Programs with Variables , 2007, LPNMR.

[50]  Frank Wolter,et al.  Semi-qualitative Reasoning about Distances: A Preliminary Report , 2000, JELIA.

[51]  Hans Tompits,et al.  Program Correspondence under the Answer-Set Semantics: The Non-ground Case , 2008, ICLP.

[52]  Vladimir Lifschitz,et al.  Nested expressions in logic programs , 1999, Annals of Mathematics and Artificial Intelligence.

[53]  Stefan Woltran,et al.  A common view on strong, uniform, and other notions of equivalence in answer-set programming* , 2007, Theory and Practice of Logic Programming.

[54]  Enrico Pontelli,et al.  Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms , 2006, AAAI.

[55]  Emilia Oikarinen,et al.  On the Equivalence of Logic Programs , 2002 .

[56]  Chiaki Sakama,et al.  Coordination in answer set programming , 2008, TOCL.

[57]  David Pearce,et al.  Minimal Logic Programs , 2007, ICLP.

[58]  David Pearce,et al.  Strongly equivalent logic programs , 2001, ACM Trans. Comput. Log..

[59]  Timo Soininen,et al.  Extending and implementing the stable model semantics , 2000, Artif. Intell..

[60]  Katsumi Inoue,et al.  Hypothetical Reasoning in Logic Programs , 1994, J. Log. Program..

[61]  Thomas Eiter,et al.  Uniform Equivalence of Logic Programs under the Stable Model Semantics , 2003, ICLP.