Compilation of static and evolving conditional knowledge bases for computing induced nonmonotonic inference relations

Several different semantics have been proposed for conditional knowledge bases R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document} containing qualitative conditionals of the form “If A, then usually B”, leading to different nonmonotonic inference relations induced by R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document}. For the notion of c-representations which are a subclass of all ranking functions accepting R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document}, a skeptical inference relation, called c-inference and taking all c-representations of R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document} into account, has been suggested. In this article, we develop a 3-phase compilation scheme for both knowledge bases and skeptical queries to constraint satisfaction problems. In addition to skeptical c-inference, we show how also credulous and weakly skeptical c-inference can be modelled as constraint satisfaction problems, and that the compilation scheme can be extended to such queries. We further extend the compilation approach to knowledge bases evolving over time. The compiled form of R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document} is reused for incrementally compiling extensions, contractions, and updates of R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document}. For each compilation step, we prove its soundness and completeness, and demonstrate significant efficiency benefits when querying the compiled version of R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal {R}$\end{document}. These findings are also supported by experiments with the software system InfOCF that employs the proposed compilation scheme.

[1]  Christoph Beierle,et al.  A Practical Comparison of Qualitative Inferences with Preferred Ranking Models , 2017, KI - Künstliche Intelligenz.

[2]  E. W. Adams,et al.  The logic of conditionals , 1975 .

[3]  Didier Dubois,et al.  Possibilistic and Standard Probabilistic Semantics of Conditional Knowledge Bases , 1997, J. Log. Comput..

[4]  Wolfgang Spohn,et al.  The Laws of Belief - Ranking Theory and Its Philosophical Applications , 2012 .

[5]  Chang Liu,et al.  Term rewriting and all that , 2000, SOEN.

[6]  Christoph Beierle,et al.  Computation and comparison of nonmonotonic skeptical inference relations induced by sets of ranking models for the realization of intelligent agents , 2018, Applied Intelligence.

[7]  Gabriele Kern-Isberner,et al.  Plausible reasoning and plausibility monitoring in language comprehension , 2017, Int. J. Approx. Reason..

[8]  B. D. Finetti La prévision : ses lois logiques, ses sources subjectives , 1937 .

[9]  Thomas Lukasiewicz,et al.  Complexity results for structure-based causality , 2001, Artif. Intell..

[10]  Pierre Marquis,et al.  Compiling propositional weighted bases , 2004, Artif. Intell..

[11]  Christoph Beierle,et al.  Towards a General Framework for Kinds of Forgetting in Common-Sense Belief Management , 2018, KI - Künstliche Intelligenz.

[12]  Wolfgang Spohn,et al.  Ordinal Conditional Functions: A Dynamic Theory of Epistemic States , 1988 .

[13]  Christoph Beierle,et al.  A Two-Level Approach to Maximum Entropy Model Computation for Relational Probabilistic Logic Based on Weighted Conditional Impacts , 2014, SUM.

[14]  Michael Minock,et al.  Z-log: Applying System-Z , 2002, JELIA.

[15]  Christoph Beierle,et al.  Comparison of Inference Relations Defined over Different Sets of Ranking Functions , 2017, ECSQARU.

[16]  P G rdenfors,et al.  Knowledge in flux: modeling the dynamics of epistemic states , 1988 .

[17]  Christoph Beierle,et al.  A Generalized Iterative Scaling Algorithm for Maximum Entropy Model Computations Respecting Probabilistic Independencies , 2018, FoIKS.

[18]  Christoph Beierle,et al.  Skeptical Inference Based on C-Representations and Its Characterization as a Constraint Satisfaction Problem , 2016, FoIKS.

[19]  Christoph Beierle,et al.  Semantical investigations into nonmonotonic and probabilistic logics , 2012, Annals of Mathematics and Artificial Intelligence.

[20]  E. Sperner Ein Satz über Untermengen einer endlichen Menge , 1928 .

[21]  Gabriele Kern-Isberner,et al.  A Thorough Axiomatization of a Principle of Conditional Preservation in Belief Revision , 2004, Annals of Mathematics and Artificial Intelligence.

[22]  R. Byrne Suppressing valid inferences with conditionals , 1989, Cognition.

[23]  Donald Nute,et al.  Counterfactuals , 1975, Notre Dame J. Formal Log..

[24]  Christoph Beierle,et al.  Compilation of Conditional Knowledge Bases for Computing C-Inference Relations , 2018, FoIKS.

[25]  Peter Gärdenfors,et al.  Knowledge in Flux: Modeling the Dynamics of Epistemic States , 2008 .

[26]  Daniel Lehmann,et al.  What does a Conditional Knowledge Base Entail? , 1989, Artif. Intell..

[27]  Didier Dubois,et al.  Preference Modeling with Possibilistic Networks and Symbolic Weights: A Theoretical Study , 2016, ECAI.

[28]  Gabriele Kern-Isberner,et al.  Belief revision with reinforcement learning for interactive object recognition , 2008, ECAI.

[29]  Gabriele Kern-Isberner,et al.  Structural Inference from Conditional Knowledge Bases , 2014, Stud Logica.

[30]  Didier Dubois,et al.  Possibility Theory and Its Applications: Where Do We Stand? , 2015, Handbook of Computational Intelligence.

[31]  Thomas Lukasiewicz,et al.  Weak nonmonotonic probabilistic logics , 2004, Artif. Intell..

[32]  Christoph Beierle,et al.  Skeptical, Weakly Skeptical, and Credulous Inference Based on Preferred Ranking Functions , 2016, ECAI.

[33]  D. Nute Topics in Conditional Logic , 1980 .

[34]  Sarit Kraus,et al.  Nonmonotonic Reasoning, Preferential Models and Cumulative Logics , 1990, Artif. Intell..

[35]  Pierre Marquis,et al.  A Knowledge Compilation Map , 2002, J. Artif. Intell. Res..

[36]  D. Dubois,et al.  Conditional Objects as Nonmonotonic Consequence Relationships , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[37]  Gabriele Kern-Isberner,et al.  Rational Inference Patterns Based on Conditional Logic , 2018, AAAI.

[38]  Mats Carlsson,et al.  An Open-Ended Finite Domain Constraint Solver , 1997, PLILP.

[39]  E. W. Adams,et al.  Probability and the Logic of Conditionals , 1966 .

[40]  Thomas Schiex,et al.  Penalty Logic and its Link with Dempster-Shafer Theory , 1994, UAI.

[41]  Gadi Pinkas,et al.  Reasoning, Nonmonotonicity and Learning in Connectionist Networks that Capture Propositional Knowledge , 1995, Artif. Intell..

[42]  Didier Dubois,et al.  Symbolic Possibilistic Logic: Completeness and Inference Methods , 2018, ECSQARU.

[43]  Judea Pearl,et al.  System Z: a Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning^ , 1990 .

[44]  Judea Pearl,et al.  Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling , 1996, Artif. Intell..

[45]  P C Wason,et al.  Reasoning about a Rule , 1968, The Quarterly journal of experimental psychology.

[46]  Gabriele Kern-Isberner,et al.  Conditionals in Nonmonotonic Reasoning and Belief Revision , 2001, Lecture Notes in Computer Science.

[47]  Donald E. Knuth,et al.  Simple Word Problems in Universal Algebras††The work reported in this paper was supported in part by the U.S. Office of Naval Research. , 1970 .

[48]  J. Paris The Uncertain Reasoner's Companion: A Mathematical Perspective , 1994 .

[49]  Pierre Marquis,et al.  Consequence Finding Algorithms , 2000 .

[50]  Christoph Beierle,et al.  A Declarative Approach for Computing Ordinal Conditional Functions Using Constraint Logic Programming , 2011, INAP/WLP.

[51]  Christoph Beierle,et al.  Properties of skeptical c-inference for conditional knowledge bases and its realization as a constraint satisfaction problem , 2017, Annals of Mathematics and Artificial Intelligence.

[52]  David Makinson,et al.  General patterns in nonmonotonic reasoning , 1994 .