Datalog vs CLIPS vs Prolog

As many programmers I studied Prolog in university, but only very little. I understand that Prolog and Datalog are closely related, but Datalog is simpler? Also, I believe that I read that Datalog does not depend on ordering of the logic clauses, but I am not sure why this is advantages. CLIPS is supposedly altogether differ开发者_开发知识库ent, but it is too subtle for me to understand. Can someone please to provide a general highlights of the languages over the other languages?

The difference between CLIPS and Prolog/Datalog is that CLIPS is a "production rule system" that operates by forward chaining: given a set of facts and rules, it will try to make every possible derivation of new facts and store those in memory. A query is then answered by checking whether it matches something in the fact store. So, in CLIPS, if you have (pseudo-syntax):

parent(X,Y) => child(Y,X)
parent(john,mary)

it will immediately derive child(mary,john) and remember that fact. This can be very fast, but puts restrictions on the possible ruleset and takes up memory.

Prolog and Datalog operate by backward chaining, meaning that a query (predicate call) is answered by trying to prove the query, i.e. running the Prolog/Datalog program. Prolog is a Turing complete programming language, so any algorithm can be implemented in it.

Datalog is a non-Turing complete subset of Prolog that does not allow, e.g., negation. Its main advantage is that every Datalog program terminates (no infinite loops). This makes it useful for so-called "deductive databases," i.e. databases with rules in addition to facts.

datalog is a subset of prolog. the subset which datalog carries has two things in mind:

1. adopt an API which would support rules and queries
2. make sure all queries terminate

prolog is Turing complete. datalog is not.

getting datalog out of the way, let's see how prolog compares with clips.

prolog's expertise is "problem solving" while clips is an "expert system". if i understand correctly, "problem solving" involves expertise using code and data. "expert systems" mostly use data structures to express expertise. see http://en.wikipedia.org/wiki/Expert_system#Comparison_to_problem-solving_systems

another way to look at it is:

expert systems operate on the premise that most (if not all) outcomes are known. all of these outcomes are compiled into data and then is fed into an expert system. give the expert system a scenario, the expert system computes the outcome from the compiled data, aka knowledge base. it's always a "an even number plus an even number is always even" kind of thinking.

problem solving systems have an incomplete view of the problem. so one starts out with modeling data and behavior, which would comprise the knowledge base (this gives justice to the term "corner case") and ends up with "if we add two to six, we end up with eight. is eight divisible by two? then it is even"