Improving explanations in knowledge-based systems: RATIONALE

The paper describes a framework, RATIONALE, for building knowledge-based diagnostic systems that explain by reasoning explicitly. Unlike most existing explanation facilities that are grafted onto an independently designed inference engine, RATIONALE behaves as though it has to deliberate over and explain to itself, each refinement step. By treating explanation as primary, RATIONALE forces the system designer to represent knowledge explicitly that might otherwise be left implicit. This includes knowledge as to why a particular hypothesis is preferred, an exception is ignored, and a global inference strategy is chosen. RATIONALE integrates explanations with reasoning by allowing a causal and/or functional description of the domain to be represented explicitly. Reasoning proceeds by constructing a hypothesis-based classification tree whose root hypothesis contains the most general diagnosis of the system. Guided by a focusing algorithm, the classification tree branches into more specific hypotheses that explain the more detailed symptoms provided by the user. As the system is used, the classification tree also forms the basis for a dynamically generated explanation tree which holds both the successful and failed branches of the reasoning knowledge. RATIONALE is implemented in Quintus Prolog with a hypertext and graphics oriented interface under NeWS. It provides an environment for tying together the processes of knowledge acquisition, system implementation and explanation of system reasoning.