lyang@jennifer.UUCP (02/10/87)
>Learn LISP and PROLOG.
When I took a class on Artificial Intelligence at Stanford (CS223, for
those who care), I figured I was ready. I knew PROLOG and LISP.
And I was all set to learn about this great thing called 'AI', at
the place where big names made it happen.
I was in for a surprise. Based on my experience, if you want
to learn about hard-core, theoretical artificial intelligence,
then you must have a strong (I mean STRONG) background in formal
logic. My understanding of PROLOG (which resembles predicate logic)
was very helpful, but it wasn't enough.
If you want to go out and build expert systems, or perform some other
intelligence engineering task, then PROLOG and LISP and a basic
grasp of logic are probably enough. But if you want to follow the
latest research (and maybe eventually do some of it), then a formal
training in logic is a must.
================================================================================
Whydoesn'titsnowintherightplaces?
--Larry Yang | *A REAL signature* _|> /\ |
lyang@sun.com,{backbone}!sun!lyang | "Limit? We don't | | | /-\ |-\ /-\
Sun Microsystems, Inc. | need no stinkin' <|_/ \_| \_/\_| |_\_|
Mountain View, California | 4-line limit! " _/ _/czhj@vax1.UUCP (02/12/87)
In article <12992@sun.uucp> lyang%jennifer@Sun.COM (Larry Yang) writes: >.... >I was in for a surprise. Based on my experience, if you want >to learn about hard-core, theoretical artificial intelligence, >then you must have a strong (I mean STRONG) background in formal >logic. This is EXACTLY the problem with AI research as it is commonly done today. (and perhaps yesterday as well). The problem is that mathematicians, logicians and computer scientists, with their background in formal logic have no other recourse than to attack the AI problem using these tools that are available to them. Perhaps this is why the field makes such slow progress? AI is an ENORMOUS problem, to say the least and research into it should not be bound by the conventional thinking that is going on. We have to look at the problem in NEW ways in order to make progress. I am strongly under the impression that people with a strict theoretical training will actually HINDER the field rather than advance it because of the constraints on the ideas that they come up with just because of their background. Now, I'm NOT saying that nobody in CS, MATH, or LOGIC is capable of original thought, however, from much of the research that is being done, and from the scope of the discussions on the NET, it seems safe to say that many people of these disciplines discount less formal accounts as frivolous. But look at the approach that LOGIC gives AI. It is a purely reductionist view, akin to studying global plate motion at the level of sub-atomic particles. It is simply the wrong level at which to approach the problem. A far more RATIONAL approach would be to integrate a number of disciplines towards the goal of understanding intelligence. COMPUTER SCIENCE has a major role because of the power of computer modeling, efficient data structures and models of efficient parallel computation. Beyond that, it seems that computer science should take a back seat. LOGIC, well, where would that fit in? Maybe at the very lowest level, but most of that is taken for granted by computer science. PHILOSOPHY tends to be a DEAD END, as can clearly be noted by the arguments going on on the NET :) Honestly, the philosophy arguments tend to get so jumbled (though logical), that they really add little to the field. COGNITIVE PSYCHOLOGY is a quickly emerging field that is producing some interesting findings, however, at this stage, it is more descriptive than anything else. There is some interesting speculation into the processes that are going on behind thought in this field, and they should be looked at carefully. However, there is simply so much fluff and pointless experiments that it takes quite a while to wade through and get anything significant. LINGUISTICS is a similar field. The work of Chomsky and others has given us some fascinating ideas and may get somewhere in terms of biological constraints on knowledge and such. Even NEUROBIOLOGY should get involved. Reasearch in this field gives us more insight into internal constraints. Furthermore, by studying people with brain disorders (both congenital and through accident) we can gain some insight into what types of structures are innate or have a SPECIFIC locus of control. In sum, I call for using many different disciplines to solve the basic problems in knowledge, learning and perception. No single approach will do. ---Ted Inoue
sher@rochester.UUCP (02/13/87)
If I didn't respond to this I'd have to work on my thesis so here
goes:
I think there seems to be something of a misconception regarding the
place of logic wrt AI and computer science in general. To start with
I will declare this:
Logic is a language for expressing mathematical constructs.
It is not a science and as far as artificial intelligence is concerned
the mathematics of logic are not very relevant. Its main feature
is that it can be used for precise expression.
So why use logic rather than a more familiar language, like english.
One can be precise in english, writers like Edgar Allen Poe, Issac
Asimov, and George Gamov all have written very precise english on a
variety of topics. However the problem is that few of us knowledge
engineers have the talent to be precise in our everyday language.
There are few great, or even very good writers among AI practitioners.
Thus for decades engineers, scientists, and statisticians have used
logic to express their ideas since even an incompetent speaker can be
clear and precise using logical formalisms. However like any language
with expressive power one can be totally incomprehensible using logic.
I have seen logical expressions that even the author did not
understand. Thus logic is not a panacea, it is merely a tool. But it
is a very useful and important tool (you can chop down trees with a
boy scout knife but I'll take an axe any day and a chain saw is even
better). Also like english or any other language the more logic you
know the more clearly and compactly you can state your ideas (if you
can avoid the temptation to use false erudition and use your document
to demonstrate your formal facility rather than what you are trying to
say). Thus if you know modal or second order logics you can express
more than you can with simple 1st order predicate calculus and you can
express it better.
Of course, not everyones goals are to express themselves clearly.
Some people's business is to confuse and obfuscate. While logic can
be put to this purpose it is easier to use english for this task. It
takes an uncommon level of expertise to be really confusing without
appearing incompetant with logic.
Note: I am not a logician but I use a lot of logic in my everyday
work which is probabilistic analysis of computer vision problems
(anyone got a job?).
--
-David Sher
sher@rochester
{allegra,seismo}!rochester!sherandrews@ubc-cs.UUCP (02/17/87)
In article <278@vax1.ccs.cornell.edu> czhj@vax1.UUCP (Ted Inoue) writes: >But look at the approach that LOGIC gives AI. It is a purely reductionist >view, akin to studying global plate motion at the level of sub-atomic >particles. It is simply the wrong level at which to approach the problem. This is too generalized. There are good applications of logic to AI, and there are bad ones. Only by knowing a lot about logic *and* the structure of the problem domain can you tell which is which. I would agree that predicate logic techniques have often been applied to problems in a way that leaves out inordinately large chunks of the domain. However, the same could be said about most AI techniques. --Jamie. ...!seismo!ubc-vision!ubc-cs!andrews "Take my shoes off & throw them in the lake"