A constraint-based approach to understanding the composition of skill

A hallmark of human cognition is the ability to compose novel behaviors from an existing repertoire of skills Newell (1990). These compositional processes range from search-based problem solving to the rapid, smoothly meshed perceptual-motor coordinations of well-practiced device interaction. In this paper we describe an approach to partially automating the composition of both semi-routine and highly skilled interactive behaviors. This approach, called Cognitive Constraint Modeling (CCM), is characterized by three principles: (a) descriptions of behavior are derived via constraint satisfaction over explicitly declared architectural, task, and strategy constraints; (b) the details of behavioral control (and therefore behavior composition) emerge in part from optimizing behavior with respect to objective functions intended to capture general strategic goals (e.g., go as fast as possible); and (c) the architectural building blocks are based on a simple ontology of resource-constrained cascaded processes. We show that these three principles jointly support modeling two important aspects of an interactive task: the overlapping and anticipatory behavior of highly skilled performance, and the hierarchical control of behavior evident earlier in practice.