A constraint-based approach to understanding the composition of skill
Richard L. Lewis, Alonso H. Vera, Andrew J. Howes · 2004
A hallmark of human cognition is the ability to com-pose 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 de-scribe an approach to partially automating the compo-sition 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 ob-jective functions intended to capture general strategic goals (e.g., go as fast as possible); and (c) the architec-tural building blocks are based on a simple ontology of resource-constrained cascaded processes. We show that these three principles jointly support modeling two im-portant aspects of an interactive task: the overlapping and anticipatory behavior of highly skilled performance, and the hierarchical control of behavior evident earlier in practice. We contrast this approach with comple-mentary approaches based on modeling the procedural learning processes themselves.