Action and Time
Most formal models used in traditional AI think of events as functions from state to state. In NL work and commercial models, however, the basic concept is of a process occupying an interval of time. We are developing a formal framework which reconciles these opposing views by integrating point- and interval- based temporal logics, thinking of actions as occupying a 4-dimensional spatiotemporal `volume', and separating deduction from prediction. This work is currently supported by NSF and NIMA, and is part of a larger project (see below)

Semantics of Diagrams
Diagrammatic and hybrid representations are of interest for education and interface design generally, and there has been much controversy in conitive science on the nature of `mental images'. However, much of the discussion is confused by the lack of a clear semantic theory. We are developing a unified formal semantics for hybrid representations which generalises both Fregean model theory of logics and a `similarity' approach to semantics of diagrams. The central idea is a strict adherence to the principle of compositionality.

Naive Geographic Reasoning
In the tradition of naive physics, we are starting (5/1/97) a project to develop a systematic axiomatic description of the useful content of many qualitative spatiotemporal concepts used in geographical thinking. The eventual aim of this effort is to create a geographic knowledge base which can be used to support a useful concept of geographic consistency. This work is currently supported by NIMA.

Nature of Expertise
The history, philosophy, and sociology of science inform us that expert knowledge is comprised of context-dependent, personally constructed, highly functional but fallible abstractions. Experts can be understood as performing a societal role that they were chosen to play as a result of a constituency selection. In this way, we propose, evolutionary epistemology, or more specifically a natural selection analogy, provides a compelling basis for believing that some expert knowledge is more than merely disposable cultural myths or highly local personal fabrications. This perspective avoids both simplistic realism and complete relativism.

Computational and Philosophical Foundations of AI and Cognitive Science
Work underway at IHMC is aimed at identifying and buttressing the computational and philosophical foundations of artificial intelligence and cognitive science. In particular, there is as yet no fully satisfactory account of exactly what makes something into a `computer', and we are developing a new approach to this question. Part of this effort is a series of detailed critiques of various mistaken, though popular, `proofs' that AI is impossible.