Parallel implementation of a class reasoner

Abstract One method to overcome the notorious efficiency problems of logical reasoning algorithms in AI has been to combine a general-purpose reasoner with several special-purpose reasoners for commonly used subtasks. In this paper we are using Schubert's (Schubert et al. 1983, 1987) method of implementing a special-purpose class reasoner. We show that it is possible to replace Schubert's preorder number class tree by a preorder number list without loss of functionality. This form of the algorithm lends itself perfectly towards a parallel implementation,1 and we describe design, coding and testing of such an implementation. Our algorithm is practically independent of the size of the class list, and even with several thousand nodes learning times are under a second and retrieval times are under 500 ms.

[1]  Marc B. Vilain,et al.  The Restricted Language Architecture of a Hybrid Representation System , 1985, IJCAI.

[2]  Bernhard Nebel,et al.  Issues of Integration and Balancing in Hybrid Knowledge Representation Systems , 1987, GWAI.

[3]  Lynn Andrea Stein,et al.  Skeptical Inheritance: Computing the Intersection of Credulous Extensions , 1989, IJCAI.

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

[5]  James Geller Propositional Representation for Graphical Knowledge , 1991, Int. J. Man Mach. Stud..

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

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

[8]  Robert M. MacGregor,et al.  A Deductive Pattern Matcher , 1988, AAAI.

[9]  James Geller,et al.  Graphical Deep Knowledge for Intelligent Machine Drafting , 1987, IJCAI.

[10]  Stephen S. Wilson Neural Computing on a One Dimensional SIMD Array , 1989, IJCAI.

[11]  Ronald J. Brachman,et al.  ON THE EPISTEMOLOGICAL STATUS OF SEMANTIC NETWORKS , 1979 .

[12]  Lokendra Shastri,et al.  Default Reasoning in Semantic Networks: A Formalization of Recognition and Inheritance , 1989, Artificial Intelligence.

[13]  William A. Woods,et al.  What's in a Link: Foundations for Semantic Networks , 1975 .

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

[15]  John R. Anderson A Spreading Activation Theory of Memory , 1988 .

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

[17]  Hector J. Levesque,et al.  Krypton: A Functional Approach to Knowledge Representation , 1983, Computer.

[18]  James Geller A knowledge representation theory for natural language graphics , 1988 .

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

[20]  Lin Padgham,et al.  A Model and Representation for Type Information and Its Use in Reasoning with Defaults , 1988, AAAI.

[21]  Richmond H. Thomason,et al.  Mixing Strict and Defeasible Inheritance , 1988, AAAI.

[22]  Lenhart K. Schubert,et al.  Accelerating Deductive Inference: Special Methods for Taxonomies, Colours and Times , 1987 .