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Title:Explanation-Based Learning of Generalized Robot Assembly Plans
Author(s):Segre, Alberto Maria
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Artificial Intelligence
Computer Science
Abstract:This thesis describes an experiment involving the application of a recently developed machine learning technique, explanation-based learning, to the robot retraining problem. Explanation-based learning permits a system to acquire generalized problem-solving knowledge on the basis of a single observed problem-solving example. The resulting computer program, called ARMS for Acquiring Robotic Manufacturing Schemata, serves as a medium for discussing issues related to this particular type of learning. This work clarifies and extends the corpus of knowledge so that explanation-based learning can be successfully applied to real-world problems.
From a machine learning perspective, ARMS is one of the more ambitious working explanation-based learning implementations to date. Unlike many other vehicles for machine-learning research, the ARMS system operates in a non-trivial domain conveying the flavor of a real robot assembly application.
From a robotics perspective, ARMS represents an important first step towards a learning-apprentice system for manufacturing. It posits a theoretically more satisfactory solution to the robot retraining problem, and offers an eventual alternative to the limitations of robot programming.
Issue Date:1987
Type:Text
Description:252 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.
URI:http://hdl.handle.net/2142/69364
Other Identifier(s):(UMI)AAI8721756
Date Available in IDEALS:2014-12-15
Date Deposited:1987


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