Language requirements for AI development [duplicate]

Question

Possible Duplicate:
Why is Lisp used for AI?

What makes a language suitable for Artificial Intelligence development?

I've heard that LISP and Prolog are widely used in this field. What features make them suitable for AI?

Answer 1

Overall I would say the main thing I see about languages "preferred" for AI is that they have high order programming along with many tools for abstraction.

It is high order programming (aka functions as first class objects) that tends to be a defining characteristic of most AI languages http://en.wikipedia.org/wiki/Higher-order_programming that I can see. That article is a stub and it leaves out Prolog http://en.wikipedia.org/wiki/Prolog which allows high order "predicates".

But basically high order programming is the idea that you can pass a function around like a variable. Surprisingly a lot of the scripting languages have functions as first class objects as well. LISP/Prolog are a given as AI languages. But some of the others might be surprising. I have seen several AI books for Python. One of them is http://www.nltk.org/book. Also I have seen some for Ruby and Perl. If you study more about LISP you will recognize a lot of its features are similar to modern scripting languages. However LISP came out in 1958...so it really was ahead of its time.

There are AI libraries for Java. And in Java you can sort of hack functions as first class objects using methods on classes, it is harder/less convenient than LISP but possible. In C and C++ you have function pointers, although again they are much more of a bother than LISP.

Once you have functions as first class objects, you can program much more generically than is otherwise possible. Without functions as first class objects, you might have to construct sum(array), product(array) to perform the different operations. But with functions as first class objects you could compute accumulate(array, +) and accumulate(array, *). You could even do accumulate(array, getDataElement, operation). Since AI is so ill defined that type of flexibility is a great help. Now you can build much more generic code that is much easier to extend in ways that were not originally even conceived.

And Lambda (now finding its way all over the place) becomes a way to save typing so that you don't have to define every function. In the previous example, instead of having to make getDataElement(arrayelement) { return arrayelement.GPA } somewhere you can just say accumulate(array, lambda element: return element.GPA, +). So you don't have to pollute your namespace with tons of functions to only be called once or twice.

If you go back in time to 1958, basically your choices were LISP, Fortran, or Assembly. Compared to Fortran LISP was much more flexible (unfortunately also less efficient) and offered much better means of abstraction. In addition to functions as first class objects, it also had dynamic typing, garbage collection, etc. (stuff any scripting language has today). Now there are more choices to use as a language, although LISP benefited from being first and becoming the language that everyone happened to use for AI. Now look at Ruby/Python/Perl/JavaScript/Java/C#/and even the latest proposed standard for C you start to see features from LISP sneaking in (map/reduce, lambdas, garbage collection, etc.). LISP was way ahead of its time in the 1950's.

Even now LISP still maintains a few aces in the hole over most of the competition. The macro systems in LISP are really advanced. In C you can go and extend the language with library calls or simple macros (basically a text substitution). In LISP you can define new language elements (think your own if statement, now think your own custom language for defining GUIs). Overall LISP languages still offer ways of abstraction that the mainstream languages still haven't caught up with. Sure you can define your own custom compiler for C and add all the language constructs you want, but no one does that really. In LISP the programmer can do that easily via Macros. Also LISP is compiled and per the programming language shootout, it is more efficient than Perl, Python, and Ruby in general.

Prolog basically is a logic language made for representing facts and rules. What are expert systems but collections of rules and facts. Since it is very convenient to represent a bunch of rules in Prolog, there is an obvious synergy there with expert systems.

Now I think using LISP/Prolog for every AI problem is not a given. In fact just look at the multitude of Machine Learning/Data Mining libraries available for Java. However when you are prototyping a new system or are experimenting because you don't know what you are doing, it is way easier to do it with a scripting language than a statically typed one. LISP was the earliest languages to have all these features we take for granted. Basically there was no competition at all at first.

Also in general academia seems to like functional languages a lot. So it doesn't hurt that LISP is functional. Although now you have ML, Haskell, OCaml, etc. on that front as well (some of these languages support multiple paradigms...).

Answer 2

The main calling card of both Lisp and Prolog in this particular field is that they support metaprogramming concepts like lambdas. The reason that is important is that it helps when you want to roll your own programming language within a programming language, like you will commonly want to do for writing expert system rules.

To do this well in a lower-level imperative language like C, it is generally best to just create a separate compiler or language library for your new (expert system rule) language, so you can write your rules in the new language and your actions in C. This is the principle behind things like CLIPS.

Answer 3

The two main things you want are the ability to do experimental programming and the ability to do unconventional programming.

When you're doing AI, you by definition don't really know what you're doing. (If you did, it wouldn't be AI, would it?) This means you want a language where you can quickly try things and change them. I haven't found any language I like better than Common Lisp for that, personally.

Similarly, you're doing something not quite conventional. Prolog is already an unconventional language, and Lisp has macros that can transform the language tremendously.

Answer 4

What do you mean by "AI"? The field is so broad as to make this question unanswerable. What applications are you looking at?

LISP was used because it was better than FORTRAN. Prolog was used, too, but no one remembers that. This was when people believed that symbol-based approaches were the way to go, before it was understood how hard the sensing and expression layers are.

But modern "AI" (machine vision, planners, hell, Google's uncanny ability to know what you 'meant') is done in more efficient programming languages that are more sustainable for a large team to develop in. This usually means C++ these days--but it's not like anyone thinks of C++ as a good language for AI.

Hell, you can do a lot of what was called "AI" in the 70s in MATLAB. No one's ever called MATLAB "a good language for AI" before, have they?