Abstract
This research seeks to .incorporate machine learning capabilities within a general-purpose frame-based architecture. We describe CHUNKER, an explanation-based chunking mechanism built on top of THEO, a software framework to support development of self-modifying problem solving systems. CHUNKER forms rules that improve problem solving efficiency, by generalizing and compressing the chains of inference which THEO produces during problem solving. After presenting the learning algorithm used by CHUNKER, we illustrate its application to learning search control knowledge, discuss its relationship to THEO’s other three learning mechanisms, and consider the relationship between architectural features of THEO and the effectiveness of CHUNKER.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Pattern Recognition and Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
