The Knowledge Level Reinterpreted: Modeling How Systems Interact

Abstract

Machine learning will never progress beyond its current state until people realize that knowledge is not a substance that can be stored. Knowledge acquisition, in particular, is a process of developing computer models, often for the first time, not a process of transferring or accessing statements or diagrams that are already written down and filed away in an expert’s mind. The “knowledge acquisition bottleneck” is a wrong and misleading metaphor, suggesting that the problem is to squeeze a large amount of already-formed concepts and relations through a narrow communication channel; the metaphor seriously misconstrues the theory formation process of computer modeling. The difficulties of choosing and evaluating knowledge acquisition methods are founded on a number of related misconceptions, clarified as follows: 1) the primary concern of knowledge engineering is modeling systems in the world (not replicating how people think—a matter for psychology); 2) knowledge-level analysis is how observers describe and explain the recurrent behaviors of a situated system, that is, some system interacting with an embedding environment; the knowledge level describes the product of an evolving, adaptive interaction between the situated system and its environment, not the internal, physical processes of an isolated system; 3) modeling intelligent behavior is fraught with frame-of-reference confusions, requiring that we tease apart the roles and points of view of the human expert, the mechanical devices he interacts with, the social and physical environment, and the observer-theoretician (with his own interacting suite of recording devices, representations, and purposes). The challenge to knowledge acquisition today is to clarify what we are doing (computer modeling), clarify the difficult problems (the nature of knowledge and representations), and reformulate our research program accordingly.