Forgetting for Answer Set Programs Revisited

A new semantic forgetting for answer set programs (ASP), called SM-forgetting, is proposed in the paper. It distinguishes itself from the others in that it preserves not only skeptical and credulous consequences on unforgotten variables, but also strong equivalence-forgetting same variables in strongly equivalent logic programs has strongly equivalent results. The forgetting presents a positive answer to Gabbay, Pearce and Valverde's open question - if ASP has uniform interpolation property. We also investigate some properties, algorithm and computational complexities for the forgetting. It shows that computing the forgetting result is generally intractable even for Horn logic programs.

[1]  Pierre Marquis,et al.  Reasoning under inconsistency: A forgetting-based approach , 2010, Artif. Intell..

[2]  Kewen Wang,et al.  Semantic forgetting in answer set programming , 2008, Artif. Intell..

[3]  Joohyung Lee,et al.  A generalization of the Lin-Zhao theorem , 2006, Annals of Mathematics and Artificial Intelligence.

[4]  David Pearce,et al.  A New Logical Characterisation of Stable Models and Answer Sets , 1996, NMELP.

[5]  Yi Zhou,et al.  Forgetting in Logic Programs under Strong Equivalence , 2012, KR.

[6]  Pedro Cabalar,et al.  Propositional theories are strongly equivalent to logic programs , 2007, Theory Pract. Log. Program..

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

[8]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[9]  Jeff Z. Pan,et al.  Forgetting for knowledge bases in DL-Lite , 2010, Annals of Mathematics and Artificial Intelligence.

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

[11]  Stefan Woltran,et al.  Characterising equilibrium logic and nested logic programs: Reductions and complexity1,2 , 2009, Theory and Practice of Logic Programming.

[12]  R. Reiter,et al.  Forget It ! , 1994 .

[13]  Yongmei Liu,et al.  On the Progression of Knowledge in the Situation Calculus , 2011, IJCAI.

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

[15]  Fangzhen Lin,et al.  ASSAT: computing answer sets of a logic program by SAT solvers , 2002, Artif. Intell..

[16]  K. Fernow New York , 1896, American Potato Journal.

[17]  Dov M. Gabbay,et al.  Interpolable Formulas in Equilibrium Logic and Answer Set Programming , 2011, J. Artif. Intell. Res..

[18]  A. Visser Uniform interpolation and layered bisimulation , 1996 .

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

[20]  Boris Konev,et al.  The Logical Difference for the Lightweight Description Logic EL , 2012, J. Artif. Intell. Res..

[21]  John Mylopoulos,et al.  Principles of Knowledge Representation and Reasoning: Proceedings of the Tenth International Conference , 2006 .

[22]  R. Lathe Phd by thesis , 1988, Nature.

[23]  F. Pfenning Theory and Practice of Logic Programming , 2014 .

[24]  Norman Y. Foo,et al.  Solving Logic Program Conflict through Strong and Weak Forgettings , 2005, IJCAI.

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

[26]  Miroslaw Truszczynski,et al.  Answer set programming at a glance , 2011, Commun. ACM.

[27]  Yan Zhang,et al.  Knowledge forgetting: Properties and applications , 2009, Artif. Intell..

[28]  Daniel Gooch,et al.  Communications of the ACM , 2011, XRDS.