On the Working De nition of Intelligence

This paper is about the philosophical and methodological foundation of arti cial intelligence (AI). After discussing what is a good \working de nition", \intelligence" is de ned as \the ability for an information processing system to adapt to its environment with insu cient knowledge and resources". Applying the de nition to a reasoning system, we get the major components of Non-Axiomatic Reasoning System (NARS), which is a symbolic logic implemented in a computer system, and has many interesting properties that are closely related to intelligence. The de nition also clari es the di erence and relationship between AI and other disciplines, such as computer science. Finally, the de nition is compared with other popular de nitions of intelligence, and its advantages are argued. 1 To De ne Intelligence

[1]  Christopher Cherniak,et al.  Minimal Rationality , 1986, Computational models of cognition and perception.

[2]  Carl G. Hempel,et al.  A purely syntactical definition of confirmation , 1943, Journal of Symbolic Logic.

[3]  Pei Wang,et al.  Belief Revision in Probability Theory , 1993, UAI.

[4]  M. Minsky.  The Society of Mind , 1986 .

[5]  Douglas B. Lenat,et al.  On the thresholds of knowledge , 1987, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

[6]  David Lindley,et al.  Logical foundations of probability , 1951 .

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

[8]  P. Smolensky.  On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.

[9]  David Kirsh,et al.  Foundations of AI: The Big Issues , 1991, Artif. Intell..

[10]  C. Peirce,et al.  Collected Papers of Charles Sanders Peirce , 1936, Nature.

[11]  D. Mart.  Artificial Intelligence-- A Personal View , 2022 .

[12]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[13]  Pei Wang,et al.  The interpretation of fuzziness , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[14]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[15]  P. Kugel,et al.  Thinking may be more than computing , 1986, Cognition.

[16]  Pei Wang.  From Inheritance Relation to Non-Axiomatic Logic , 1994 .

[17]  Lawrence Birnbaum,et al.  Rigor Mortis: A Response to Nilsson's "Logic and Artificial Intelligence" , 1991, Artif. Intell..

[18]  John R. Searle,et al.  Minds, brains, and programs , 1980, Behavioral and Brain Sciences.

[19]  Jay K. Strosnider,et al.  A Structured View of Real-Time Problem Solving , 1994, AI Mag..

[20]  Nils J. Nilsson,et al.  Logic and Artificial Intelligence , 1991, Artif. Intell..

[21]  Grigoris Antoniou,et al.  Nonmonotonic reasoning , 1997 .

[22]  Stuart J. Russell,et al.  Do the right thing , 1991 .

[23]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[24]  Mark S. Boddy,et al.  Deliberation Scheduling for Problem Solving in Time-Constrained Environments , 1994, Artif. Intell..

[25]  John McCarthy,et al.  What Computers Still Can't Do , 1996, Artif. Intell..

[26]  Michael Miller,et al.  Memory, Reason, and Time: the Step-logic Approach , 1991 .

[27]  Drew McDermott,et al.  A critique of pure reason 1 , 1987, The Philosophy of Artificial Intelligence.

[28]  James L. McClelland,et al.  PDP models and general issues in cognitive science , 1986 .

[29]  Douglas R. Hofstadter,et al.  Godel, Escher, Bach: An Eternal Golden Braid. , 1980 .

[30]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[31]  Allen Newell,et al.  Computer science as empirical inquiry: symbols and search , 1976, CACM.

[32]  G. Edelman,et al.  Real brains and artificial intelligence , 1989 .

[33]  Melanie Mitchell,et al.  The Copycat project: a model of mental fluidity and analogy-making , 1995 .

[34]  David J. Chalmers,et al.  High-level perception, representation, and analogy: a critique of artificial intelligence methodology , 1992, J. Exp. Theor. Artif. Intell..

[35]  Pei Wang.  A Defect in Dempster-Shafer Theory , 1994, UAI.