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