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diff --git a/doc/awkforai.txt b/doc/awkforai.txt index 3fca3204..2908da56 100644 --- a/doc/awkforai.txt +++ b/doc/awkforai.txt @@ -1,150 +1,18 @@ -Draft for ACM SIGPLAN Patterns (Language Trends) +Sun Dec 9 11:58:23 IST 2012 +============================ -1996 +The original of this file was a draft of an article written in 1996 by +Ronald P. Lui for an ACM Sigplan publication, explaining why he used +gawk for teaching introductory Artificial Intelligence courses. -Why GAWK for AI? +Since it was not clear as to copying permissions and so on, it has been +removed from the gawk distribution. -Ronald P. Loui +A quick web search shows that this same draft is available at +http://www.cs.wustl.edu/~loui/sigplan and the final article is available +from the ACM: http://dl.acm.org/citation.cfm?id=242908 -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). +The article itself is recommended reading. -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). +Arnold Robbins +arnold@skeeve.com |