Proceedings of the Annual Meeting of the Cognitive Science Society

This paper describes a space of possible models of knowledge-lean human problem solving characterised by the use of recognition knowledge to control search. Recognition-based Problem Solvers (RPS) are contrasted to Soar and ACT - R which tend to use large goal stacks to control search and to situated theories of cognition that tend not to be able to do search at all (e.g. Pengi). It is shown that with appropriate knowledge increments R P S can apply algorithms such as depth-first search with a bounded demand on Working Memory . The discussion then focuses on h o w some weak methods, such as depth- first search, are more difficult to encode in R P S than others. It is claimed that the difficulty of encoding depth-first reflects human performance.