Learning from Textbook Knowledge: A Case Study

William W. Cohen

One of the "grand challenges for machine learning" is the problem of learning from textbooks. This paper addresses the problem of learning from texts including omissions and inconsistencies that are clarified by illustrative examples. To avoid problems in natural language understanding, we consider a simplification of this problem in which the text has been manually translated into a logical theory. This learning problem is solvable by a technique that we call analogical abductive explanation based learning (ANA-EBL). Formal evidence and experimental results in the domain of contract bridge show that the learning technique is both efficient and effective.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.