Machine Intelligence 14 Applied Machine Intelligence

Name: Machine Intelligence 14 Applied Machine Intelligence
Price: 10.89 USD
Availability: InStock
ISBN: 9780198538608

ISBN-10: 019853860X

ISBN-13: 9780198538608

Edition: 1995

Authors: K. Furukawa, D. Michie, S. Muggleton

List price: $150.00

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Description:

Machine Intelligence 14 contains material presented at the Anglo-Janpanese workshop of Novemver 1993 held at the Hitachi Research Laboratory. It marks the 70th birthday of Donald Michie, the founder of the series. The contents is divided into the following subjects: complex decision taking, inductive logic programming, applied machine learning, dynamic control, and computational learning theory. Applications include controlling a steel mill, discovery of protein structural constraints, and qualitative control for dynamic systems.

Book details

List price: $150.00
Copyright year: 1995
Publisher: Oxford University Press, Incorporated
Publication date: 8/8/1996
Binding: Hardcover
Pages: 470
Size: 6.14" wide x 9.21" long x 1.22" tall
Weight: 1.892
Language: English




Game mastery and intelligence



Reacting, planning, and learning in an autonomous agent



Multi-entity models



Putting knowledge rich plan represetnations to use



On dealing with dynamic utility of learned knowledge



Inverting entailment and progol



Learning logic programs and regularities from examples by inductive inference



Variations and local exceptions in inductive logic programming



Inductive logic programming with large-scale unstructured data



Discovery of protein structural constraints in a deductive database using inductive logic programming



Controlling a steel mill with Boxes



Ta Reasoning System For Legal Analogy



Concept learning algorithm with adaptive search



Deriving qualitative control for dynamic systems



Behavioural clones and cognitive skill models



A classification of abduction: abduction for logic programming



Efficient algorithms for inductive learning - an application of multi-linear functions to inductive learning