Abstract:
This thesis proposes and implements a new Theory Revision System. The Theory
Revision problem is defined as the problem of how best to go about revising a
knowledge base on the basis of a collection of examples, some of which expose
inaccuracies in the original knowledge base. This problem entreats a thorough
investigation of the following machine learning field of study: Combining Inductive
and Analytical learning. The problem of theory revision has been studied for quite
some time and various systems have been proposed. On one hand, there are successful
theory revision systems like EITHER and PTR, which combirie Inductive and
Analytical Learning. On the other hand, there are mention-worthy systems like
KBANN, TANGENTPROaPn,d EBNN that use imperfect domain theories together with
given training set of data. The new system is built by incorporating Version Spacebased
Incremental Probabilistic Evidence Combination method and Integrated
AnalyticallEmpirical method. The proposed system is constructed to maXImIze
preservation of already gained meaningful information. To our knowledge, Version
Space-based approach has not been applied for theory revision problem as yet.
Experimental results show that the performance of the new system is comparable with
other fairly successful systems.
