Pamela McCorduck wrote in "Machines Who Think" (2004) that Cyc is "a determinedly scruffy enterprise". Robert Abelson credited the terms to his "unnamed but easily guessable colleague" Roger Shank in his 1981 essay "Constraint, Construal, and Cognitive Science" in the Proceedings of the 3rd Annual Conference of the Cognitive Science, and Marvin Minsky discusses the terms in Patrick Henry Winston's 1990 book "Artificial Intelligence at MIT, Expanding Frontiers, Vol 1", and his own 1991 AI Magazine article, "Logical Versus Analogical or Symbolic Versus Connectionist or Neat Versus Scruffy", but the long standing terms go back to the 70's:

https://ojs.aaai.org/aimagazine/index.php/aimagazine/article...

https://ojs.aaai.org/aimagazine/index.php/aimagazine/article...

"We should take our cue from biology rather than physics..." -Marvin Minsky

https://grandtextauto.soe.ucsc.edu/2008/02/14/ep-44-ai-neat-...

EP 4.4: AI, Neat and Scruffy

by Noah Wardrip-Fruin, 6:11 am

A name that does appear in Weizenbaum’s book, however, is that of Roger Schank, Abelson’s most famous collaborator. When Schank arrived from Stanford to join Abelson at Yale, together they represented the most identifiable center for a particular approach to artificial intelligence: what would later (in the early 1980s) come to be known as the “scruffy” approach. [7] Meanwhile, perhaps the most identifiable proponent of what would later be called the “neat” approach, John McCarthy, remained at Stanford.

McCarthy had coined the term “artificial intelligence” in the application for the field-defining workshop he organized at Dartmouth in 1956. Howard Gardner, in his influential reflection on the field, The Mind’s New Science (1985), characterized McCarthy’s neat approach this way: “McCarthy believes that the route to making machines intelligent is through a rigorous formal approach in which the acts that make up intelligence are reduced to a set of logical relationships or axioms that can be expressed precisely in mathematical terms” (154).

This sort of approach lent itself well to problems easily cast in formal and mathematical terms. But the scruffy branch of AI, growing out of fields such as linguistics and psychology, wanted to tackle problems of a different nature. Scruffy AI built systems for tasks as diverse as rephrasing newspaper reports, generating fictions, translating between languages, and (as we have seen) modeling ideological reasoning. In order to accomplish this, Abelson, Schank, and their collaborators developed an approach quite unlike formal reasoning from first principles. One foundation for their work was Schank’s “conceptual dependency” structure for language-independent semantic representation. Another foundation was the notion of “scripts” (later “cases”) an embryonic form of which could be seen in the calling sequence of the ideology machine’s executive. Both of these will be considered in more detail in the next chapter.

Scruffy AI got attention because it achieved results in areas that seemed much more “real world” than those of other approaches. For comparison’s sake, consider that the MIT AI lab, at the time of Schank’s move to Yale, was celebrating success at building systems that could understand the relationships in stacks of children’s wooden blocks. But scruffy AI was also critiqued — both within and outside the AI field — for its “unscientific” ad-hoc approach. Weizenbaum was unimpressed, in particular, with the conceptual dependency structures underlying many of the projects, writing, “Schank provides no demonstration that his scheme is more than a collection of heuristics that happen to work on specific classes of examples” (199). Whichever side one took in the debate, there can be no doubt that scruffy projects depending on coding large amounts of human knowledge into AI systems — often more than the authors acknowledged, and perhaps much more than they realized.

[...]

[7] After the terms “neat” and “scruffy” were introduced into the AI and cognitive science discourse by Abelson’s 1981 essay, in which he attributes the coinage to “an unnamed but easily guessable colleague” — Schank.

https://cse.buffalo.edu/~rapaport/676/F01/neat.scruffy.txt

Article: 35704 of comp.ai
From: engelson@bimacs.cs.biu.ac.il (Dr. Shlomo (Sean) Engelson)
Newsgroups: comp.ai
Subject: Re: who first used "scruffy" and "neat"?
Date: 25 Jan 1996 08:17:13 GMT
Organization: Bar-Ilan University Computer Science

In article <4e2th9$lkm@cantaloupe.srv.cs.cmu.edu> Lonnie Chrisman <ldc+@cs.cmu.edu> writes:

so@brownie.cs.wisc.edu (Bryan So) wrote:
>A question of curiosity. Who first used the terms "scruffy" and "neat"?
>And in what document? How about "strong" and "weak"?

Since I don't see a response yet, I'll take a stab. The earliest use of
"scruffy" and "neat" that comes to my mind was in David Chapman's "Planning
for Conjunctive Goals", Artificial Intelligence 32:333-377, 1987. "Weak"
evidence for this being the earliest use is that he does not cite any earlier
use of the terms, but perhaps someone else will correct me and give an
earlier citation.

One earlier citation is Eugene Charniak's paper in AAAI 1986, "A Neat
Theory of Marker Passing". I think, though, that the terms go way
back in common parlance, almost certainly to the 70s at least. Any of
the "old-timers" out there like to comment?

[...]

Article: 35781 of comp.ai
From: fass@cs.sfu.ca (Dan Fass)
Newsgroups: comp.ai
Subject: Re: who first used "scruffy" and "neat"?
Date: 26 Jan 1996 10:03:35 -0800
Organization: Simon Fraser University, Burnaby, B.C.

Abelson (1981) credits the neat/scruffy distinction to Roger Schank.
Abelson says, ``an unnamed but easily guessable colleague of mine
... claims that the major clashes in human affairs are between the
"neats" and the "scruffies". The primary concern of the neat is
that things should be orderly and predictable while the scruffy
seeks the rough-and-tumble of life as it comes'' (p. 1).

Abelson (1981) argues that these two prototypic identities --- neat
and scruffy --- ``cause a very serious clash'' in cognitive science
and explores ``some areas in which a fusion of identities seems
possible'' (p. 1).

- Dan Fass

REF

Abelson, Robert P. (1981).
Constraint, Construal, and Cognitive Science.
Proceedings of the 3rd Annual Conference of the Cognitive Science
Society, Berkeley, CA, pp. 1-9.

[...]

Aaron Sloman, 1989: "Introduction: Neats vs Scruffies"

https://www.cs.bham.ac.uk//research/projects/cogaff/misc/scr...

>There has been a long-standing opposition within AI between "neats" and
"scruffies" (I think the terms were first invented in the late 70s by
Roger Schank and/or Bob Abelson at Yale University).

>The neats regard it as a disgrace that many AI programs are complex,
ill-structured, and so hard to understand that it is not possible to
explain or predict their behaviour, let alone prove that they do what
they are intended to do. John McCarthy in a televised debate in 1972
once complained about the "Look Ma no hands!" approach. Similarly, Carl
Hewitt, complained around the same time, in seminars, about the "Hairy
kludge (pronounced klooge) a month" approach to software development.
(His "actor" system was going to be a partial solution to this.)

>The scruffies regard messy complexity as inevitable in intelligent
systems and point to the failure so far of all attempts to find workable
clear and general mechanisms, or mathematical solutions to any important
AI problems. There are nice ideas in the General Problem Solver, logical
theorem provers, and suchlike but when confronted with non-toy problems
they normally get bogged down in combinatorial explosions. Messy
complexity, according to scruffies, lies in the nature of problem
domains (e.g. our physical environment) and only by using large numbers
of ad-hoc special-purpose rules or heuristics, and specially tailored
representational devices can problems be solved in a reasonable time.

Roger Schank

https://en.wikipedia.org/wiki/Roger_Schank

Robert Abelson

https://en.wikipedia.org/wiki/Robert_Abelson

Marvin Minsky

https://en.wikipedia.org/wiki/Marvin_Minsky

Neats and scruffies

https://en.wikipedia.org/wiki/Neats_and_scruffies

>Scruffy projects in the 1980s

>The scruffy approach was applied to robotics by Rodney Brooks in the mid-1980s. He advocated building robots that were, as he put it, Fast, Cheap and Out of Control, the title of a 1989 paper co-authored with Anita Flynn. Unlike earlier robots such as Shakey or the Stanford cart, they did not build up representations of the world by analyzing visual information with algorithms drawn from mathematical machine learning techniques, and they did not plan their actions using formalizations based on logic, such as the 'Planner' language. They simply reacted to their sensors in a way that tended to help them survive and move.[13]

>Douglas Lenat's Cyc project was initiated in 1984 one of earliest and most ambitious projects to capture all of human knowledge in machine readable form, is "a determinedly scruffy enterprise".[14] The Cyc database contains millions of facts about all the complexities of the world, each of which must be entered one at a time, by knowledge engineers. Each of these entries is an ad hoc addition to the intelligence of the system. While there may be a "neat" solution to the problem of commonsense knowledge (such as machine learning algorithms with natural language processing that could study the text available over the internet), no such project has yet been successful.

[...]

>John Brockman writes "Chomsky has always adopted the physicist's philosophy of science, which is that you have hypotheses you check out, and that you could be wrong. This is absolutely antithetical to the AI philosophy of science, which is much more like the way a biologist looks at the world. The biologist's philosophy of science says that human beings are what they are, you find what you find, you try to understand it, categorize it, name it, and organize it. If you build a model and it doesn't work quite right, you have to fix it. It's much more of a "discovery" view of the world."[4]