Distinguishing Abduction and Induction under Intensional Complexity

1 Universitat Politècnica de València, Departament de Sistemes Informàtics i Computació, Camí de Vera 14, Aptat. 22.012 E-46071, València, Spain. E-mail: jorallo@dsic.upv.es. On-line papers: http://www.dsic.upv.es/~jorallo/escrits/escritsa.htm. 2 Universitat Politècnica de València, Institut Tecnològic d’Informàtica, Camí de Vera 14, E-46071, València, Spain. E-mail: ivarea@iti.upv.es Abstract: This paper presents a theoretical and general differentiation among descriptional induction, explanatory induction and abduction. Descriptional induction is based on the idea of compression (justified by meanor cross-validation). Explanatory induction is characterised by a 'balanced' compression (exception-free validation). Finally, abduction is the more elusive notion, where the validation comes from a background theory. Since this background theory can also be used in both kinds of induction, we must distinguish between an auxiliary use and a necessary or ‘consilient’ use of the background knowledge. We introduce many new concepts and formalisations for this goal, mainly the idea of ‘intrinsic exception or anomaly’, consilience and an operative measure of reinforcement for logic programs. Finally, the difference between induction and abduction is seen in the context of growth of knowledge and theory revision.

[1]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[2]  K. Wexler The Subset Principle is an Intensional Principle , 1993 .

[3]  P. Thagard,et al.  Explanatory coherence , 1993 .

[4]  Atocha Aliseda,et al.  A Unified Framework for Abductive and Inductive Reasoning in Philosophy and AI , 1996 .

[5]  Paolo Mancarella,et al.  Abductive Logic Programming , 1992, LPNMR.

[6]  Marc Denecker,et al.  AILP: Abductive Inductive Logic Programming , 1995, IJCAI.

[7]  M. Resnik,et al.  Aspects of Scientific Explanation. , 1966 .

[8]  Douglas R. Hofstadter,et al.  Godel, Escher, Bach: An Eternal Golden Braid , 1981 .

[9]  Kurt Konolige,et al.  Abduction Versus Closure in Causal Theories , 1992, Artif. Intell..

[10]  Craig Boutilier,et al.  Abduction as Belief Revision , 1995, Artif. Intell..

[11]  David B. Leake Abduction, experience, and goals: a model of everyday abductive explanation , 1995, J. Exp. Theor. Artif. Intell..

[12]  Judea Pearl,et al.  Belief Networks Revisited , 1993, Artif. Intell..

[13]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[14]  J. Reggia,et al.  Abductive Inference Models for Diagnostic Problem-Solving , 1990, Symbolic Computation.

[15]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

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

[17]  Peter A. Flach,et al.  Abduction and induction: syllogistic and inferential perspectives , 1996 .

[18]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[19]  Antonis C. Kakas,et al.  Learning Abductive Theories , 1996 .

[20]  Hwee Tou Ng,et al.  On the Role of Coherence in Abductive Explanation , 1990, AAAI.

[21]  Rudi Studer Natural Language and Logic , 1990, Lecture Notes in Computer Science.

[22]  Robert H. Ennis Enumerative Induction and Best Explanation , 1968 .

[23]  Jorma Rissanen,et al.  Fisher information and stochastic complexity , 1996, IEEE Trans. Inf. Theory.

[24]  Peter Grünwald,et al.  The Minimum Description Length Principle and Non - Deductive Inference , 1997 .

[25]  David Poole,et al.  On the Comparison of Theories: Preferring the Most Specific Explanation , 1985, IJCAI.

[26]  P. Thagard,et al.  Probabilistic Networks and Explanatory Coherence. , 1997 .

[27]  Mark E. Stickel,et al.  Rationale and Methods for Abductice Reasoning in Natural-Language Interpretation , 1989, Natural Language and Logic.

[28]  Dean Allemang,et al.  The Computational Complexity of Abduction , 1991, Artif. Intell..

[29]  Ehud Shapiro,et al.  Inductive Inference of Theories from Facts , 1991, Computational Logic - Essays in Honor of Alan Robinson.

[30]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

[31]  David Poole,et al.  Explanation and prediction: an architecture for default and abductive reasoning , 1989, Comput. Intell..

[32]  O. Penrose The Direction of Time , 1962 .

[33]  Chong Ho Yu,et al.  Abduction? Deduction? Induction? Is There a Logic of Exploratory Data Analysis?. , 1994 .

[34]  Ming Li,et al.  On Prediction by Data Compression , 1997, ECML.

[35]  C. Hartshorne,et al.  Collected Papers of Charles Sanders Peirce , 1935, Nature.

[36]  Paul Thagard,et al.  The Best Explanation: Criteria for Theory Choice , 1978 .

[37]  K. Popper,et al.  Conjectures and refutations;: The growth of scientific knowledge , 1972 .

[38]  A. Kolmogorov Three approaches to the quantitative definition of information , 1968 .

[39]  Paul O'Rorke,et al.  Coherence and abduction , 1989, Behavioral and Brain Sciences.

[40]  Raymond J. Mooney,et al.  Integrating Abduction and Induction in Machine Learning , 2000 .

[41]  A. P. van den Bosch Simplicity and Prediction , 1994 .