MTLS: a tool for extending and refining knowledge bases

The paper presents an interactive multistrategy learning system (MTLS) that extends and refines knowledge bases by learning from input examples, discovering new knowledge, and cooperating with a user. The use of the multistrategy learning approach based on plausible justification trees allows MTLS to perform learning tasks that are beyond the capability of a single strategy learning method. A goal driven knowledge discovery method has been developed and integrated into MTLS to produce additional knowledge needed by the system. MTLS also allows a human expert to guide it to refine and extend the knowledge base. This cooperation between a human expert and the learner enables the system to perform tasks that are intrinsically difficult for an autonomous system. The resulting knowledge base may include new rules discovered from data, as well as revised rules, and new facts learned by analogy. MTLS has been developed as a tool to be used by a domain expert to build a knowledge base to reduce the need for assistance from a knowledge engineer