AI has frequently been criticized for being `stuck in the microworld' because of the common inability of AI systems to cope with the complexity of real domains. Often, adding details removes regularity, transforming a representation from a few simple structures to a large, unwieldy collection of specialized ones.
This paper addresses this problem in the context of representing planning operators (domain-specific knowledge about the effects of actions in a domain) for use by AI planning systems. We present a novel approach in which domain-specific operators are represented as a composition of general components, and show that the problem of manually building a detailed set of operators can be avoided by constructing them from a small number of such components instead. Each component encapsulates information about a domain feature that might be modeled, and each may contribute to several operators. Moreover, we describe how the choice of what to model and what to ignore in a domain can then be easily varied, simply by controlling which components are used. Finally, we show how operator sets built in this way can be used by planning algorithms.
