The Programmer's Apprentice: A Session with KBEmacs
IEEE Transactions on Software Engineering - Special issue on artificial intelligence and software engineering
Design of Knowledge-Based Systems with a Knowledge-Based Assistant
IEEE Transactions on Software Engineering - Special Issue on Artificial Intelligence in Software Applications
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Proceedings of the fourth international conference on Genetic algorithms
Proceedings of the fourth international conference on Genetic algorithms
Survey of expert critiquing systems: practical and theoretical frontiers
Communications of the ACM
Seven Layers of Knowledge Representation and Reasoning in Support of Software Development
IEEE Transactions on Software Engineering - Special issue on knowledge representation and reasoning in software development
Artificial Intelligence as the year 2000 approaches
Communications of the ACM
Speculation on the evolution of intelligence and its possible realization in machine form.
Speculation on the evolution of intelligence and its possible realization in machine form.
Assessing creative problem-solving with automated text grading
Computers & Education
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Expert systems are a well-known and well-received technology. It was thought that the performance of a domain expert could not be duplicated by a machine. Expert systems technologies have shown this to be a false belief, and indeed have demonstrated how experts themselves can come to depend on expert systems. Expert systems enjoy widespread use in industrial domains and further uses are planned. The growth in acceptance has been explosive since about 1986. Continued rampant growth appears to depend on cracking the so-called knowledge acquisition bottleneck.The knowledge acquisition bottleneck limits the scalability of expert systems. While it is relatively straightforward to populate a small-scale knowledge base, it becomes more difficult to maintain consistency and validity as the knowledge base grows. Thus, it is important to automate the knowledge acquisition process. A by-product of this process is that any failure of the expert system will be “soft.”The question is, “What impact can machine learning technologies have on knowledge acquisition in the large?” The true test will be on prospective industrial applications in areas such as biology, education, geology, medicine, and scientific discovery. Machine learning technologies include expert systems, genetic algorithms, neural networks, random seeded crystal learning, or any effective combinations.Relevant subtopics include:Second generation expert systems — progress and prognosisRepertory GridsThe importance of symbolic and qualitative reasoningThe acquisition of fuzzy rulesThe best learning paradigm or combination of paradigmsImpact of machine learning on explanation systemsThe role of toy domains such as chessAutomatic programming revisitedApplications to computer vision, decision support systems, diagnosis, helpdesks, optimization, planning, scheduling, et al.Implementation issues using SIMD and MIMD platformsSources for joint sponsorshipForming industrial partnershipsForming alliances abroad