A Learning System and Its Psychological Implications

ACT is a computer simulation program that uses a propositional network to represent knowledge of facts and a set of productions (condition - action rules) to represent knowledge of procedures. There are currently four different mechanisms by which ACT can make additions and modifications to its set of productions: designation, strengthening, generalization, and discrimination. Designation refers to the ability of productions to call for the creation of new productions. Strengthening a production involves adjusting the amount of system resources that will be allocated to its processing. Finally, generalization and discrimination refer to complementary processes that produce better performance by either extending or restricting the range of situations in which a production will apply. Theae learning mechanisms are used to simulate experiments on prototype formation. ACT successfully accounts for the effects of distance of instances from a central tendency, frequency of individual instances, and the family resemblance structure of categories.

[1]  Allen Newell,et al.  Production Systems: Models of Control Structures , 1973 .

[2]  Stuart C. Shapiro,et al.  A Net Structure for Semantic Information Storage, Deduction and Retrieval , 1971, IJCAI.

[3]  John R. Anderson,et al.  A General Learning Theory and its Application to Schema Abstraction1 , 1979 .

[4]  Donald A. Norman,et al.  Explorations in Cognition , 1975 .

[5]  E. Rosch,et al.  Family resemblances: Studies in the internal structure of categories , 1975, Cognitive Psychology.

[6]  Steven A. Vere,et al.  Induction of Relational Productions in the Presence of Background Information , 1977, IJCAI.

[7]  R. Brown A First Language , 1973 .

[8]  Donald A. Waterman,et al.  Generalization Learning Techniques for Automating the Learning of Heuristics , 1970, Artif. Intell..

[9]  F. Hayes-Roth,et al.  Concept learning and the recognition and classification of exemplars , 1977 .

[10]  J. Bransford,et al.  Abstraction of visual patterns. , 1971, Journal of experimental psychology.

[11]  John D. Bransford,et al.  The abstraction of linguistic ideas , 1971 .

[12]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[13]  John R. Anderson,et al.  Complex Learning Processes , 1978 .

[14]  M. Ross Quillian,et al.  The teachable language comprehender: a simulation program and theory of language , 1969, CACM.

[15]  P. G. Neumann An attribute frequency model for the abstraction of prototypes , 1974, Memory & cognition.

[16]  Charles L. Forgy,et al.  A Production System Monitor for Parallel Computers. , 1977 .

[17]  H. Dreyfus What Computers Can't Do , 1972 .

[18]  John R. Anderson,et al.  A Theory of the Acquisition of Cognitive Skills. , 1978 .

[19]  Gerald Jay Sussman,et al.  A Computer Model of Skill Acquisition , 1975 .

[20]  G. Bower,et al.  Storage and later recognition of exemplars of concepts , 1973 .