Decision Planning Knowledge Representation Framework: A Case-Study

This paper discusses experiences and perspectives of utilisation of declarative knowledge structures as a convenient knowledge base medium in configuration expert systems. Although many successful systems have been developed, these are often difficult to maintain and to generalize in rapidly changing domains. In this paper we address the problem of building intelligent knowledge based systems with emphasis on their maintainability. Firstly, several industrial applications of proof planning, a theorem proving technique, will be described and their advantages and flaws will be discussed. This discussion is followed by the theoretical foundation of decision planning knowledge representation framework that, based on proof planning, facilitates separate administration of inference problem solving knowledge and the domain theory axioms. Machine learning methods for maintaining the inference knowledge to be up-to-date with permanently changing domain theory are commented and evaluated.