Schematic interpretation and the CLARET consolidated learning algorithm

Abstract

A new learning algorithm, the Consolidated Learning Algorithm based on Relational Evidence Theory (CLARET) is presented, which relies on inductive logic programming and graph matching techniques. In relational evidence theory, two different approaches to evidential learning are consolidated in how they apply to generalising within relational data structures. Attribute-based discrimination (decision trees) is integrated with part-based interpretation (graph matching) for evaluating and updating representations in spatial domains. This allows an interpretation stage to be incorporated into the generalization process. A systematic approach to finding interpretations is demonstrated for an online, hand drawn, schematic diagram and symbols recognition system. The approach uses an adaptive representational bias and search strategy during learning by efficiently grounding the learning procedures in the relational spatial constraints of their application.