Buch, Englisch, Band 886, 190 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 318 g
Buch, Englisch, Band 886, 190 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 318 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-540-58811-5
Verlag: Springer Berlin Heidelberg
This research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem complexity.
In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners.
In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners.
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Research
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Overview.- The problem solver.- Generation of problem solving cases.- Case storage: Automated indexing.- Efficient case retrieval.- Analogical replay.- Empirical results.- Related work.- Conclusion.
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