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The distinction between neat and scruffy originated in the mid-1970s, by Roger Schank.Schank used the terms 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 ...
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 ...
This article is based on material taken from neats vs. scruffies at the Free On-line Dictionary of Computing prior to 1 November 2008 and incorporated under the "relicensing" terms of the GFDL, version 1.3 or later.
Controversies arose from early on in symbolic AI, both within the field—e.g., between logicists (the pro-logic "neats") and non-logicists (the anti-logic "scruffies")—and between those who embraced AI but rejected symbolic approaches—primarily connectionists—and those outside the field. Critiques from outside of the field were primarily ...
Download QR code; Print/export Download as PDF; Printable version; In other projects Wikidata item; ... Neats and scruffies; O. The Outer Limits (1995 TV series) P.
Neats vs. scruffies, in the field of artificial intelligence, a school of thought that prefers empiricism to formalism Scruffy, a graphical library in Ruby programming language Walter H. Longton (1892–1927), English First World War flying ace and later air racer
Hypercube-based NEAT, or HyperNEAT, [1] is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm developed by Kenneth Stanley. [2]
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting ...