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Review of Expert Micros
Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.
Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut
Selfridge, Mallory, Dickerson, Donald J., Biggs, Stanley F.
In order for next-generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning. We call such next-generation systems cognitive expert systems. Research at the Artificial Intelligence Laboratory at the University of Connecticut is directed toward understanding the principles underlying cognitive expert systems and developing computer programs embodying those principles. The Causal Model Acquisition System (CMACS) learns causal models of physical mechanisms by understanding real-world natural language explanations of those mechanisms. The going Concern Expert ( GCX) uses business and environmental knowledge to assess whether a company will remain in business for at least the following year. The Business Information System (BIS) acquires business and environmental knowledge from in-depth reading of real-world news stories. These systems are based on theories of expert human reasoning and learning, and thus represent steps toward next-generation cognitive expert systems.
Decision analysis: a Bayesian approach
Chapman and Hall. See also: Influence diagrams for Bayesian decision analysis, European Journal of Operational Research, Volume 40, Issue 3, 15 June 1989, Pages 363–376 (http://www.sciencedirect.com/science/article/pii/0377221789904293). Bayesian Decision Analysis: Principles and Practice, Cambridge University Press, 2010 (https://books.google.com/books/about/Bayesian_Decision_Analysis.html?id=O1lXnQAACAAJ).
Learning general search control from outside guidance,
Golding, A. R., Rosenbloom, P. S., Laird, J. E.
Proc. Tenth Intl. Joint Conference on Artificial Intelligence (pp. 334-337). Milan, Italy: IJCAII.
A logical approach to reasoning by analogy
In IJCAI-87, Proc. Tenth Intl. Joint Conference on Artificial Intelligence, Milan, Italy: Morgan Kaufmann, 1987, Vol. 1, pp. 264-270.
Why a Diagram is (sometimes) Worth Ten Thousand Words
When two representations are informationally equivalent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their capabilities for recognizing patterns, in the inferences they can carry out directly, and in their control strategies (in par- ticular, the control of search). Cognitive Science 11, 65-99
Connectionist architectures for artificial intelligence
Fahhnan, Scott | Hinton, Geoffrey
IEEE Computer, pages 100-109, January