A Connectionist Approach to Knowledge Representation and Limited Inference

Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with problems in knowledge representation and inference has often been questioned. This paper partly answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach. The paper presents a connectionist realization of semantic networks, that is, it describes how knowledge about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter-free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions and conflicting multiple inheritance situations during inheritance, and the best-match or partial-match computation during recognition. The paper also identifies constraints that must be satisfied by the conceptual structure in order to arrive at an efficient parallel realization.

[1]  Ronald J. Brachman,et al.  "I Lied About the Trees", Or, Defaults and Definitions in Knowledge Representation , 1985, AI Mag..

[2]  E. Rosch.  Cognitive Representations of Semantic Categories. , 1975 .

[3]  M. Black.  Philosophy in America , 1965 .

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

[5]  Ray Jackendoff.  Semantics and Cognition , 1983 .

[6]  A. Tversky,et al.  Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment , 1983 .

[7]  Dana H. Ballard,et al.  Parallel Logical Inference and Energy Minimization , 1986, AAAI.

[8]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[9]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[10]  Henry E. Kyburg,,et al.  The Reference Class , 1983, Philosophy of Science.

[11]  Lenhart K. Schubert,et al.  Determining Type, Part, Color, and Time Relationships , 1983, Computer.

[12]  Wayne A. Wickelgren,et al.  Chunking and consolidation: A theoretical synthesis of semantic networks configuring in conditioning , 1979 .

[13]  A. Goldman.  Epistemology and Cognition , 1986 .

[14]  Eugene Charniak,et al.  Passing Markers: A Theory of Contextual Influence in Language Comprehension* , 1983 .

[15]  Raymond Reiter,et al.  On Inheritance Hierarchies With Exceptions , 1983, AAAI.

[16]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[17]  David S. Touretzky,et al.  The Mathematics of Inheritance Systems , 1984 .

[18]  Ronald J. Brachman,et al.  An overview of the KL-ONE Knowledge Representation System , 1985 .

[19]  James F. Allen,et al.  Knowledge Retrieval as Limited Inference , 1982, CADE.

[20]  Daniel G. Bobrow,et al.  On Overview of KRL, a Knowledge Representation Language , 1976, Cogn. Sci..

[21]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[22]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[23]  Eugene Charniak,et al.  A Common Representation for Problem-Solving and Language-Comprehension Information , 1981, Artif. Intell..

[24]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[25]  L. Rips,et al.  Suppositions and the analysis of conditional sentences , 1978 .

[26]  Lokendra Shastri,et al.  Semantic Networks: An Evidential Formalization and Its Connectionist Realization , 1988 .

[27]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[28]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[29]  Terrence J. Sejnowski,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cognitive Sciences.

[30]  E. T. Jaynes,et al.  Where do we Stand on Maximum Entropy , 1979 .

[31]  Eugene Charniak,et al.  The Bayesian Basis of Common Sense Medical Diagnosis , 1983, AAAI.

[32]  John R. Anderson.  The Architecture of Cognition , 1983 .

[33]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[34]  Scott E. Fahlman,et al.  NETL: A System for Representing and Using Real-World Knowledge , 1979, CL.

[35]  P. Hayes.  The Logic of Frames , 1981 .