Computation and Intelligence: Collected Readings
George F. Luger
AAAI Press, 1995 - Computers - 735 pages
Computation and Intelligence brings together 29 readings in Artificial Intelligence that are particularly relevant to today's student/practitioner. With its helpful critique of the selections, extensive bibliography, and clear presentation of the material, Computation and Intelligence will be a useful adjunct to any course in AI as well as a handy reference for professionals in the field.
The book is divided into five parts, each reflecting the stages of development of AI. The first part, Foundations,, contains readings that present or discuss foundational ideas linking computation and intelligence, typified by A. M. Turing's "Computing Machinery and Intelligence." The second part, Knowledge Representation, presents a sampling of numerous representational schemes by Newell, Minsky, Collins & Quillian, Winograd, Schank, Hayes, Holland, McClelland, Rumelhart, Hinton, and Brooks.
The third part, Weak Method Problem Solving, fouses on the research and design of syntax-based problem solvers, including the most famous of these, the Logic Theorist and GPS. The fourth part, Reasoning in Complex and Dynamic Environments, presents a broad spectrum of the AI community's research in knowledge-intensive problem solving, from McCarthy's early design of systems with "common sense" to model-based reasoning.
The two concluding selections, by Marvin Minsky and by Herbert Simon, respectively, present the recent thoughts of two of AI's pioneers who revisit the concepts and controversies that have developed during the evolution of the tools and techniques that make up the current practice of Artificial Intelligence.
Results 1-3 of 26
Hillsdale, N.J.: Lawrence Erlbaum. Kolodner, J. L. (Ed.). (1988). Proceedings of
the DARPA case-based reasoning workshop, 1. San Mateo, CA: Morgan
Kaufmann. Kolodner, J. L., & Simpson, R. L. (1989). The mediator: Analysis of an
early case-based problem solver. Cognitive Science, 13(4), 507-549. Kolodner,
J. L., Simpson, R. L., & Sycara, K. (1985). A process model of case-based
reasoning in problem solving. In Proceedings of the ninth international joint
conference on artificial ...
Proteus: An activationframeworkfor cognitive process models (Tech. Rep. No. ISI/
WP-2). Marina del Rey, CA: University of Southern California, Information
Sciences Institute. Levine, R. D., & Tribus, M. (1979). The maximum entropy
formalism. Cambridge, MA: MIT Press. Lewis, A. C. (1986). Memory constraints
and flower choice in pieris rapae. Science, 232, 863- 865. Lewis, C. H. (1978).
Production system models of practice effects. Unpublished doctoral dissertation,
University of ...
Levels indeed! A response to Broadbent. Journal of Experimental Psychology:
General, 1 14, 193-197. Rumelhart, D. E., & Norman, D. A. (1982). Simulating a
skilled typist: A study of skilled cognitive-motor performance. Cognitive Science, 6
, 1 -36. Rumelhart, D. E., & Zipser, D. (1985). Feature discovery by competitive
learning. Cognitive Science, 9, 75- 112. Rumelhart, D. E., Lindsay, P. H., &
Norman, D. A. (1972). A process model for long-term memory. In E. Tulving & W.
What people are saying - Write a review
given a world of three statement A,B,C write statement "Almaz is student" who has a pen has pencil using the predicate student(x) to mean x is student and has (x,y) to mean x has y
Computing Machinery and Intelligence
Part TwoKnowledge Representation
A Framework for Representing Knowledge
22 other sections not shown