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+Draft for ACM SIGPLAN Patterns (Language Trends)
+
+1996
+
+Why GAWK for AI?
+
+Ronald P. Loui
+
+Most people are surprised when I tell them what language we use in our
+undergraduate AI programming class.  That's understandable.  We use
+GAWK.  GAWK, Gnu's version of Aho, Weinberger, and Kernighan's old
+pattern scanning language isn't even viewed as a programming language by
+most people.  Like PERL and TCL, most prefer to view it as a "scripting
+language."  It has no objects; it is not functional; it does no built-in
+logic programming.  Their surprise turns to puzzlement when I confide
+that (a) while the students are allowed to use any language they want;
+(b) with a single exception, the best work consistently results from
+those working in GAWK.  (footnote:  The exception was a PASCAL
+programmer who is now an NSF graduate fellow getting a Ph.D. in
+mathematics at Harvard.) Programmers in C, C++, and LISP haven't even
+been close (we have not seen work in PROLOG or JAVA).
+
+Why GAWK?
+
+There are some quick answers that have to do with the pragmatics of
+undergraduate programming.  Then there are more instructive answers that
+might be valuable to those who debate programming paradigms or to those
+who study the history of AI languages.  And there are some deep
+philosophical answers that expose the nature of reasoning and symbolic
+AI.  I think the answers, especially the last ones, can be even more
+surprising than the observed effectiveness of GAWK for AI.
+
+First it must be confessed that PERL programmers can cobble together AI
+projects well, too.  Most of GAWK's attractiveness is reproduced in
+PERL, and the success of PERL forebodes some of the success of GAWK.
+Both are powerful string-processing languages that allow the programmer
+to exploit many of the features of a UNIX environment.  Both provide
+powerful constructions for manipulating a wide variety of data in
+reasonably efficient ways.  Both are interpreted, which can reduce
+development time.  Both have short learning curves.  The GAWK manual can
+be consumed in a single lab session and the language can be mastered by
+the next morning by the average student.  GAWK's automatic
+initialization, implicit coercion, I/O support and lack of pointers
+forgive many of the mistakes that young programmers are likely to make.
+Those who have seen C but not mastered it are happy to see that GAWK
+retains some of the same sensibilities while adding what must be
+regarded as spoonsful of syntactic sugar.  Some will argue that
+PERL has superior functionality, but for quick AI applications, the
+additional functionality is rarely missed.  In fact, PERL's terse syntax
+is not friendly when regular expressions begin to proliferate and
+strings contain fragments of HTML, WWW addresses, or shell commands.
+PERL provides new ways of doing things, but not necessarily ways of
+doing new things.
+
+In the end, despite minor difference, both PERL and GAWK minimize
+programmer time.  Neither really provides the programmer the setting in
+which to worry about minimizing run-time.
+
+There are further simple answers.  Probably the best is the fact that
+increasingly, undergraduate AI programming is involving the Web.  Oren
+Etzioni (University of Washington, Seattle) has for a while been arguing
+that the "softbot" is replacing the mechanical engineers' robot as the
+most glamorous AI testbed.  If the artifact whose behavior needs to be
+controlled in an intelligent way is the software agent, then a language
+that is well-suited to controlling the software environment is the
+appropriate language.  That would imply a scripting language.  If the
+robot is KAREL, then the right language is "turn left; turn right." If
+the robot is Netscape, then the right language is something that can
+generate "netscape -remote 'openURL(http://cs.wustl.edu/~loui)'" with
+elan.
+
+Of course, there are deeper answers.  Jon Bentley found two pearls in
+GAWK:  its regular expressions and its associative arrays.  GAWK asks
+the programmer to use the file system for data organization and the
+operating system for debugging tools and subroutine libraries.  There is
+no issue of user-interface.  This forces the programmer to return to the
+question of what the program does, not how it looks.  There is no time
+spent programming a binsort when the data can be shipped to /bin/sort
+in no time.  (footnote:  I am reminded of my IBM colleague Ben Grosof's
+advice for Palo Alto:  Don't worry about whether it's highway 101 or 280.
+Don't worry if you have to head south for an entrance to go north.  Just
+get on the highway as quickly as possible.)
+
+There are some similarities between GAWK and LISP that are illuminating.
+Both provided a powerful uniform data structure (the associative array
+implemented as a hash table for GAWK and the S-expression, or list of
+lists, for LISP).  Both were well-supported in their environments (GAWK
+being a child of UNIX, and LISP being the heart of lisp machines).  Both
+have trivial syntax and find their power in the programmer's willingness
+to use the simple blocks to build a complex approach.
+
+Deeper still, is the nature of AI programming.  AI is about
+functionality and exploratory programming.  It is about bottom-up design
+and the building of ambitions as greater behaviors can be demonstrated.
+Woe be to the top-down AI programmer who finds that the bottom-level
+refinements, "this subroutine parses the sentence," cannot actually be
+implemented.  Woe be to the programmer who perfects the data structures
+for that heapsort when the whole approach to the high-level problem
+needs to be rethought, and the code is sent to the junkheap the next day.
+
+AI programming requires high-level thinking.  There have always been a few
+gifted programmers who can write high-level programs in assembly language.
+Most however need the ambient abstraction to have a higher floor.  
+
+Now for the surprising philosophical answers.  First, AI has discovered
+that brute-force combinatorics, as an approach to generating intelligent
+behavior, does not often provide the solution.  Chess, neural nets, and
+genetic programming show the limits of brute computation.  The
+alternative is clever program organization.  (footnote: One might add
+that the former are the AI approaches that work, but that is easily
+dismissed:  those are the AI approaches that work in general, precisely
+because cleverness is problem-specific.)  So AI programmers always want
+to maximize the content of their program, not optimize the efficiency
+of an approach.  They want minds, not insects.  Instead of enumerating
+large search spaces, they define ways of reducing search, ways of
+bringing different knowledge to the task.  A language that maximizes
+what the programmer can attempt rather than one that provides tremendous
+control over how to attempt it, will be the AI choice in the end.
+
+Second, inference is merely the expansion of notation.  No matter whether
+the logic that underlies an AI program is fuzzy, probabilistic, deontic,
+defeasible, or deductive, the logic merely defines how strings can be 
+transformed into other strings.  A language that provides the best
+support for string processing in the end provides the best support for
+logic, for the exploration of various logics, and for most forms of
+symbolic processing that AI might choose to call "reasoning" instead of
+"logic."  The implication is that PROLOG, which saves the AI programmer
+from having to write a unifier, saves perhaps two dozen lines of GAWK
+code at the expense of strongly biasing the logic and representational
+expressiveness of any approach.
+
+I view these last two points as news not only to the programming language
+community, but also to much of the AI community that has not reflected on
+the past decade's lessons.
+
+In the puny language, GAWK, which Aho, Weinberger, and Kernighan thought
+not much more important than grep or sed, I find lessons in AI's trends,
+AI's history, and the foundations of AI.  What I have found not only
+surprising but also hopeful, is that when I have approached the AI
+people who still enjoy programming, some of them are not the least bit
+surprised.
+
+
+R. Loui (loui@ai.wustl.edu) is Associate Professor of Computer Science,
+at Washington University in St. Louis.  He has published in AI Journal,
+Computational Intelligence, ACM SIGART, AI Magazine, AI and Law, the ACM
+Computing Surveys Symposium on AI, Cognitive Science, Minds and
+Machines, Journal of Philosophy, and is on this year's program
+committees for AAAI (National AI conference) and KR (Knowledge
+Representation and Reasoning).