Explanation-Based Learning of Generalized Robot Assembly Plans.
Abstract
This report 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 nontrivial domain conveying the flavor of a real robot assembly application. (Keywords: Artificial intelligence; Scenarios).
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1987
- Accession Number
- ADA182969
Entities
People
- Alberto M. Segre
Organizations
- University of Illinois Urbana–Champaign