PATHFINDER is a system that solves coordination problems that require acquisition of a convention governing the intended meaning of a symbol. LEADER blazes a trail through a maze by leaving symbols in the various paths, and FOLLOWER must find LEADER by discovering the Intended meanings of these blazes. PATHFINDER is the first step In a project to design a system that can solve a variety of coordination problems of the sort implicated in language acquisition. Solving certain coordination problems is conmuni eating. Since coordination problem solution can become conventional (as David Lewis has shown), communication can become conventional, and that is language in its most general form. As conventions are acquired, more sophisitcated coordination problems can be solved, and more sophisticated conventions can be acquired. Eventually, it should be possible to acquire conventions governing identifiers and general terms, and this will enable use of a first order language via a recursive procedure adapted from Tarski by Cummins.

There is currently a competition between two theories that propose to explain the cognitive phenomenon of analogy: Dedre Centner's Structure-Mapping Theory and Douglas Hofstadter's theory of Analogy as High-level Perception. W e argue that the competition between the two theories is ill-founded because they arc after two different aspects of analogy: structure-mapping seeks a "horizontal" view of analogy where the phenomena is examined at the level of already existing psychological representations, and where the task is to identify what processes are common to all or most analogy function; High-level Perception, on the other hand, seeks a "vertical" view of analogy in which the goal is to explain the processes that make up the construction of represenUtions. An integrated theory of analogy should encompass both horizontal and vertical views.

Previous computational theories of problem solving have not accounted for the occasional display of accelerated problem solving by humans working on conceptually hard problems. Researchers refer to this behavior as insight. Kaplan and Simon describe insight as the selection of a good representation of the problem by the problem solver. They propose a dual-state space theory, meta-space theory, to explain insight (lO^lan and Simon, 1990). W e show that meta-space theory is unfalsifiable. W e then show that the nature of meta-space theory makes it superfluous for the study of human problem solving.