Neat Versus Scruffy: How Early AI Researchers Classified Epistemic Cultures of Knowledge Representation

Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at instit...

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Bibliographic Details
Published inIEEE annals of the history of computing Vol. 47; no. 2; pp. 7 - 19
Main Author Poirier, Lindsay
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2025
Subjects
Artificial intelligence
Computational modeling
Human intelligence
Interviews
Knowledge representation
Oral communication
Organizations
Psychology
Stability analysis
Symbols
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ISSN1058-6180
1934-1547
DOI10.1109/MAHC.2024.3498692

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Summary:Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at institutions in the United States and Europe to document epistemic and esthetic differences in approaches to knowledge representation. In this article, I historicize and situate these binary oppositions, showing how researchers leveraged them in discourse to demarcate the beliefs and commitments underpinning certain AI approaches. I argue that the labels helped knowledge representation scholars identify, trouble, and grapple with the epistemological and stylistic pluralism of the field, resituate epistemic legitimacy and authority with certain design approaches, and in turn constitute the boundaries of symbolic AI.
ISSN:1058-6180
1934-1547
DOI:10.1109/MAHC.2024.3498692