Neats

Neat and scruffy are labels for two different types of artificial intelligence (AI) research. Neats consider that solutions should be elegant, clear and provably correct. Scruffies believe that intelligence is too complicated (or computationally intractable) to be solved with the sorts of homogeneous system such neat requirements usually mandate.

Much success in AI came from combining neat and scruffy approaches. For example, there are many cognitive models matching human psychological data built in Soar and ACT-R. Both of these systems have formal representations and execution systems, but the rules put into the systems to create the models are generated ad hoc.

The distinction was originally made by Roger Schank in the mid-1970s to characterize the difference between his work on natural language processing (which represented commonsense knowledge in the form of large amorphous semantic networks) from the work of John McCarthy, Allen Newell, Herbert A. Simon, Robert Kowalski and others whose work was based on logic and formal extensions of logic. Roger Schank actually notes that he originally made this distinction in linguistics, related to Chomskian vs. non-Chomskian, but discovered it works in AI too, and other areas.

The distinction was also partly geographical and cultural: "scruffy" was associated with AI research at MIT under Marvin Minsky in the 1960s. The laboratory was famously "freewheeling" and researchers often developed AI programs by spending long hours tweaking programs until they showed the required behavior. This practice was named "hacking" and the laboratory gave birth to the hacker culture. Important and influential "scruffy" programs developed at MIT included Joseph Weizenbaum'sELIZA, which behaved as if it spoke English, without any formal knowledge at all, and Terry Winograd'sSHRDLU, which could successfully answer queries and carry out actions in a simplified world consisting of blocks and a robot arm.SHRDLU, while enormously successful, could not be scaled up into a useful natural language processing system, however, because it had no overarching design and maintaining a larger version of the program proved to be impossible; it was too scruffy to be extended.

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