Unification and data binding, in this context, are different things. Prolog:

  gives(tom, book, SOMEONE) :-
    person(SOMEONE),
    likes(tom, SOMEONE).

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Let's look at the like( tom, SOMEONE ) bit. Prolog would fetch all of the like/2 facts and attempt to unify those facts, one at a time, with like( tom, SOMEONE ). As soon as a fact matches (unifies), then the SOMEONE variable will be bound to the corresponding value. So if we have these facts:

  likes( mary, tom ).
  likes( tom, susan ).

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The first fact won't unify and SOMEONE won't be bound to tom. When the second fact is encountered, unification occurs and SOMEONE is bound to the value susan. Once that's bound, then the engine goes to person(SOMEONE) and since the variable is already bound, we are attempting to unify this fact with a fact in the database which asserts that susan is, in fact, a person. If we find that fact, we get to the top of the rule and know that tom gives a book to susan.

So the terminology is right, but this means we tend to have exhaustive, depth-first backtracking searches. If we have lots of facts in our database, these searches can be very slow. Having done a lot of work in this area before, I know how incredibly common (and slow) the binding and unbinding of data can be, so while I generally tell people not to do premature optimization, like many others, I caution folks that this isn't always true. If you're writing a ray-tracing program and you need it to be fast, you know you're probably not going to reach for Perl first. (Of course, if you want predicate logic, you also don't want to reach for Perl first, but I'm strange that way).

Cheers,
Ovid

New address of my CGI Course.

In reply to Re^2: Efficient partial deep cloning by Ovid
in thread Efficient partial deep cloning by Ovid

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