How can we integrate interrelated theories of individual elements of cognition? Computational models of reasoning processes encode an understanding of reasoning. Consequently, a computational modeling language may be ideally suited to the presentation of theories of cognition. By representing theories of a variety of phenomena in a single modeling language, we can potentially explore how these theories might interact. The Task-Method-Knowledge (TMK) modeling language evolves from artificial intelligence research on the subject of multistrategy reasoning. TMK models provide a compositional account of reasoning processes; they describe not only what the elements of a process are, but also how the functional properties of these elements combine to form the functional properties of the process as a whole. This paper explores the composition of theories of cognition within the TMK framework, drawing on some existing theories within cognitive science as examples.

In this paper we explore the idea of reflection in the context of scientific exploration. How does an agent reflect upon its behavior in order to enable productive exploration? We outline an abstract cognitive architecture for combining reflection and exploration. To achieve this we present a language for modeling cognition: the Task-Method-Knowledge (TMK) language. We further present a computational model based on this language, TORQUE2 (Griffith etal., 1997; Griffith, 1997). TORQUE2 is a model of exploratory reasoning in the domain of scientific problem solving. We claim that the TMK language supports both reflection and exploration, and enables them to benefit from one another.

We investigate some aspects of cognition involved in invention, more precisely in the invention of the telephone by Alexander Graham Bell. We propose the use of the Structure-Behavior-Function (SBF) language for the representation of invention knowledge; we claim that because SBF has been shown to support a wide range of reasoning about physical devices, it constitutes a plausible account of how an inventor might represent knowledge of an invention. We further propose the use of the ACT-R architecture for the implementation of this model. ACT-R has been shown to very precisely model a wide range of human cognition. We draw upon the architecture for execution of productions and matching of declarative knowledge through spreading activation. Thus we present a model which combines the well-established cognitive validity of ACT-R with the powerful, specialized model-based reasoning methods facilitated by SBF.