Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design

ion level relative to the information processing task, some control issues are artifacts of the representation. In our opinion these are often misinterpreted as issues at the knowledge level. For example, rule-based approaches often concern themselves with syntactic conflict resolution strategies. When the knowledge is viewed at the appropriate level, we can often see the existence of organizations of knowledge that bring up only a small, highly relevant body of knowledge without any need for conflict resolution at all. Of course, these organizational constructs could be "programmed" in the rule language (metarules are meant to do this in rule-based systems), but because of the status assigned to the rules and their control as knowledge-level phenomena (as opposed to the implementation-level phenomena, which they often are), knowledge acquisition is often directed toward strategies for conflict resolution, whereas they ought to be directed to issues of knowledge organization. This is not to argue that rule representations and backwardor forward-chaining controls are not natural for some situations. If all a problem solver has in the form of knowledge in a domain is a large collection of unorganized associative patterns, then data-directed or goal-directed associations may be the best the agent can do. But that is precisely the occasion for weak methods such as hypothesize -and-match (of which the above associations are variants), and, typically, successful solutions cannot be expected in complex problems without combinatorial searches. Typically, however, expertise consists of much better organized collections of knowledge, with control behavior indexed by the kinds of organization and forms of knowledge they contain. We have found six generic tasks that are very useful as building blocks for the construction (and understanding) of knowledge-based systems. These tasks cover a wide range of existing expert systems. Because of their role as building blocks, we call them elementary generic tasks. While we have been adding to our repertoire of elementary generic tasks for quite some time, the basic elements of the framework have been in place for a number of years. In particular, our work on MDX4,5 identified hierarchical classification, hypothesis matching, and knowledge-directed information passing as three generic tasks and showed how certain classes of diagnostic problems can be implemented as an integration of these generic tasks. (In the past we have also referred to them as problem-solving types.) Over the years we have identified several others: object synthesis by plan selection and refinement,6 state abstraction,7 and abductive assembly of hypotheses.8 This list is not exhaustive; in fact, our ongoing research objective is to identify other useful generic tasks and understand their knowledge representation and control