In the scientist’s house are many mansions... Outsiders often regard science as a sober enterprise, but we who are inside see it as the most romantic… - Pat Langley

A cognitive architecture specifies aspects of an intelligent system that are stable over time, much as in a building’s architecture. These include the memories that store perceptions, beliefs, and knowledge, the representation of elements that are contained in these memories, the performance mechanisms that use them, and the learning processes that build on them. Such a framework typically comes with a programming language and software environment that supports the efficient construction of knowledge-based systems.

BACON.4 does not have heuristics for considering trigonometric functions of variables directly . Thus, in the run described here we simply told the system to examine the sines. In the following chapter we will see how BACON can actually arrive at the sine term on its own in a rather subtle manner.

Research on cognitive architectures varies widely in the degree to which it attempts to match psychological data. ACT-R (Anderson & Lebiere, 1998) and EPIC (Kieras & Meyer, 1997) aim for quantitative fits to reaction time and error data, whereas Prodigy (Minton et al., 1989) incorporates selected mechanisms like means-ends analysis but otherwise makes little contact with human behavior. Architectures like Soar (Laird, Newell, & Rosenbloom, 1987; Newell, 1990) and Icarus (Langley & Choi, in press; Langley & Rogers, 2005) take a middle position, drawing on many psychological ideas but also emphasizing their strength as flexible AI systems. What they hold in common is an acknowledgement of their debt to theoretical concepts from cognitive psychology and a concern with the same intellectual abilities as humans.