Open AccessPartial Evaluation, Programming Methodology, and Artificial Intelligence
Author(s) -
Kahn Kenneth M.
Publication year - 1984
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v5i1.425
Subject(s) - computer science , artificial intelligence , inductive programming , symbolic programming , dependency (uml) , process (computing) , dual (grammatical number) , partial evaluation , interpreter , programming paradigm , representation (politics) , programming language , software engineering , art , literature , politics , political science , law
This article presents a dual dependency between AI and programming methodologies AI is an important source of ideas and tools for building sophisticated support facilities which make possible certain programming methodologies These advanced programming methodologies in turn can have profound effects upon the methodology of AI research Both of these dependencies are illustrated by the example of a new experimental programming methodology which is based upon partial evaluation Partial evaluation is based upon current AI ideas about reasoning, representation, and control The manner in which AI systems are designed, developed and tested can be significantly improved in the programming is supported by a sufficiently powerful partial evaluator In particular, the process of building levels of interpreters and of intertwining generate and test can be partially automated Finally, speculations about a more direct connection between AI and partial evaluation are presented.