Michael R. Genesereth
Much of one’s knowledge of a task domain is in the form of simple facts and procedures. While these facts and procedures may vary from domain to domain, there is often substantial similarity in the "abstract structure" of the knowledge. For example, the notion of a hierarchy is found in biological taxonomy, the geological classification of time, and the organization chart of a corporation. One advantage of recognizing such abstractions is that they can be used in selecting metaphors and models that are computationally very powerful and efficient. This power and efficiency can be used in evaluating plausible hypotheses about new domains and can thereby motivate the induction of abstractions even in the face of partial or inconsistent data. Furthermore, there is a seductive argument for how such information processing criteria can be used in characterizing "intuitive" thought and in explaining the cogency of causal arguments. The idea of large-scale, unified knowledge structures like abstractions is not a new one. The gestalt psychologists (e.g. [Kohler]) had the intuition decades ago, and recently Kuhn [Kuhn], Minsky [Minsky], and Schank [Schank & Abelson] have embodied similar intuitions in their notions of paradigms, frames, and scripts. (See also [Bobrow & Norman] and [Moore & Newell] for related ideas.) The novelty here lies in the use of such structures to select good metaphors and models and in the effects of the resulting power and efficiency on cognitive behavior. This paper describes a particular formalization of abstractions -in a knowledge representation system called ANALOG and shows how abstractions can be used in model building, understanding and generating analogies, and theory formation. The presentation here is necessarily brief and mentions only the highlights. The next section defines the notions of abstraction and simulation structure. Section 3 describes the use of abstractions in building computational models, and section 4 shows how abstractions can bc used to gain power as well as efficiency.