text: Languages Much of the early Frame language research (e.g. Schank and Abelson) had been driven by findings from experimental psychology and attempts to design knowledge representation tools that corresponded to the patterns humans were thought to use to function in daily tasks. These researchers were less interested in mathematical formality since they believed such formalisms were not necessarily good models for the way the average human conceptualizes the world. The way humans use language for example is often far from truly logical.
  Similarly, in linguistics, Charles J. Fillmore in the mid-1970s started working on his theory of frame semantics, which later would lead to computational resources like FrameNet. Frame semantics was motivated by reflections on human language and human cognition.
  Researchers such as Ron Brachman on the other hand wanted to give AI researchers the mathematical formalism and computational power that were associated with Logic. Their aim was to map the Frame classes, slots, constraints, and rules in a Frame language to set theory and logic.  One of the benefits of this approach is that the validation and even creation of the models could be automated using theorem provers and other automated reasoning capabilities. The drawback was that it could be more difficult to initially specify the model in a language with a formal semantics.
  This evolution also illustrates a classic divide in AI research known as the "neats vs. scruffies". The "neats" were researchers who placed the most value on mathematical precision and formalism which could be achieved via First Order Logic and Set Theory. The "scruffies" were more interested in modeling knowledge in representations that were intuitive and psychologically meaningful to humans.