AndersonJ.R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
2.
AndersonJ.ThompsonR. (1989). Use of analogy in a production system architecture. InVosniadouS.OrtonyA., (Eds.), Similarity and analogy. New York: Cambridge University Press.
3.
BookerL.B.GoldbergD.E.HollandJ.H. (1989). Classifier systems and genetic algorithms. Artificial Intelligence. 40, 235–282.
4.
BuchananB.C.ShortliffeE.H., (Eds.) (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley.
5.
EstesW.K. (1988). Toward a framework for combining connectionist and symbol-processing models. Journal of Memory and Language, 27, 196–212.
6.
EstesW.K. (1991). Cognitive architectures from the standpoint of an experimental psychologist, Annual Review of Psychology, 41, 1–28.
GoldbergD.E. (1983). Computer-aided gas pipeline operation using genetic algorithms and rule learning. Ph.D. dissertation, University Microfilms No. 8402282, University of Michigan, Ann Arbor.
9.
Johnson-LairdP. (1983). Menial models. Cambridge, MA: Harvard University Press.
LairdJ.E.HuckaM.YagerE.S.TuckC.M. (1990). Correcting and extending domain knowledge using outside guidance. Proceedings of the Seventh International Conference on Machine Learning.
12.
LairdJ.E.RosenbloomP.S. (1990). Integrating execution, planning, and learning in SOAR for external environments. Proceedings of the 1990 National Conference of the American Association for Artificial Intelligence.
13.
LehmanJ.F.LewisR.L.NewellA. (1991). Natural language comprehension in SOAK: Spring 1991. School of Computer Science, CMU-CS-91-IH. Pittsburgh: Carnegie-Mellon University.
14.
LenatD.B.FeigenbaumE.A. (1987). On the thresholds of knowledge. Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Milan, August 23–28, 1987 (pp.1173–1182). Los Altos, CA: Morgan Kaufman.
15.
LewisR.L.NewellA.PolkT.A. (1989). Toward a SOAR theory of taking instructions for immediate reasoning tasks. Proceedings of the Eleventh Annual Conference of the Cognitive Science Society (pp.514–521).
16.
LindsayR.K. (1963). Inferential memory as the basis of machines which understand natural language. InFeigenbaumE.A.FeldmanJ, (Eds.), Computers and thought (pp.217–233). New York: McGraw-Hill.
17.
LindsayR.K. (1973). In defense of ad hoc systems. InSchankR.ColbyK., (Eds.), Computer models of thought and language (pp.372–395). San Francisco: W.H. Freeman.
18.
LindsayR.K. (1988). Images and inference. Cognition, 29, 229–250.
19.
LindsayR.K.BuchananE.G.FeigenbaumE.A.LederbergJ. (1980). Applications of artificial intelligence for organic chemistry: The DENDRAL project. New York: McGraw-Hill.
20.
McCarthyJ. (1979). First order theories of individual concepts and propositions. InMichieD., (Ed.)Machine intelligence9, Edinburgh: Edinburgh University Press.
21.
McCarthyJ. (1980). Circumscription–A form of Artificial Intelligence, 13, 27–39.
22.
McCloskeyM.CohenN.J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. Psychological Learning and Motivation: Advances in Research and Theory, 24, 109–165.
23.
MinskyM. (1986). The society of minds. New York: Simon and Sehuster.
24.
NewellA. (1973). Production systems: Models of control structures. InChaseW.C., (Ed.), Visual information processing (pp.463–526). New York: Academic Press.
25.
NewellA. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
26.
NewellA.ShawJ.C.SimonH.A. (1985). Elements of a theory of human problem solving. Psychological Review, 65, 151–166.
27.
NewellA.SimonH.A. (1963). OPS, a program that simulates human thought. InFeigenbaumE.A.FeldmanJ., (Eds.), Computers and thought (pp.279–293). New York: McGraw-Hill.
PalmerS. (1978). Aspects of representation. InRoschE.LloydB.B., (Eds.), Computing and categorization (pp.259–303). Hillsdale, NJ; Erlbaum.
30.
PowellD.J.TongS.S.SkolnickM.M. (1989). EnGENEous domain independent, machine learning for design optimization. Proceedings of the Third International Conference on Genetic Algorithms. Los Altos, CA: Morgan Kaufman.
31.
PowersW.T. (1973). Behavior: The control of perception. Chicago. Aldine.
32.
PowersW.T. (1973). Qualitative analysis of purposive systems: Some spadework at the foundations of scientific psychology. Psychological Review, 85, 417–435.
33.
RatcliffR. (1990). Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychological Review, 98, 285–308.
34.
RosenbloomP.S. (1983). The chunking of goal hierarchies: A model of practice and stimulus-response compatibility. Pittsburgh, PA: Ph.D. dissertation. Computer Science Department, Carnegie-Mellon University.
35.
RumelhartD.E.McClellandJ.L. (1986). On learning the past lenses of English verbs. InRumelhartD.E.McClellandJ.L., and the PDF research group. Parallel distributed processing: Explorations in the microstructure of cognition, 2, 216–271.
36.
RumelhartD.E.McClellandJ.L., and the POP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition (3 vols.), Cambridge, MA: MIT Press.
37.
SejnowskiT.J.RosenbergC.R. (1987). Parallel networks that learn to pronounce English text. Complex Systems, 1, 145–168.
38.
SimonH.A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 26, 467–482.
39.
SimonH.A. (1990). Models of my life. Cambridge, MA; Harvard University Press.
40.
SmolenskyP. (1987a). On variable binding and the representation of symbolic structures in connectionist systems. Technical Report CU-CS-355-87, Department of Computer Science, University of Colorado at Boulder.
41.
SmolenskyP. (1987b). The constituent structure of connectionist mental states: A reply to Fodor and Pylyshyn. Southern Journal of Philosophy, 26, 137–163. (Special issue on connectionism and the foundations of cognitive science).
42.
SmolenskyP. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74.
43.
SmolenskyP. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159–216.
44.
TouretzkyD.S. (1990). BoltzCONS: Dynamic symbol structures in a connectionist network. Artificial Intelligence, 46, 5–46.