Prolog in Python - Part 3

Arithmetic operations

The final version of our Prolog interpreter implements a few of the arithmetic operators as well as the "cut" and "fail" terms. Up to now we've been unable to do any numerical computation. Actually, this is not Prolog's strong point anyway. But some numerical computation is always necessary. For example here is a pair of rules to find the length of a list.

length([H|T],N) :- length(T,Nt), N is Nt+1

This says that the length of an empty list is zero and the length of any other list is one greater than the length of its tail (the list less its first term).

The interesting term is "N is Nt+1". For one thing it's in infix. There is an equivalent form "is(N,+(Nt,1))" which looks more Prolog-like (and Lisp-like) but is harder to read. It turns out that it requires only a few extra lines of Python to implement the infix form, although the operator "is" is renamed (internally only) to "is" since "is" should remain a valid name.

It is necessary to discuss these operations in some detail. Terms in Prolog are used as goals within rules and either succeed or fail. During unification variables are sometimes set as goals succeed. With "N is Nt+1", or better, "is(N,+(Nt,1))", first the inner term must succeed before the outer term is tried.

The '+' operator succeeds only if both arguments evaluate to numbers. The term then evaluates to the sum of the numbers. Operators like '-', '*'. '/' work in exactly the same way. The boolean operators "<", "==", etc. also expect numeric arguments but then simply succeed or fail. They are only used as the top term in a goal.

The 'is' operator is a combination of both the Python "=" and the '==' operators. A variable on the left that is unset is set to the computation on the right and the term succeeds. If the left side is already set then the term succeeds only if the two sides are equal. Although it's not obvious at this point, this lets us do both of the following.

? length([a,b,c],X)?
{'X': 3}
? length([a,b,c],3)?

Code changes for arithmetic

Click here to access the Python code for You may find it convenient to save it into a file and access it with a text editor.

In we used the split function in the string module to split terms in a rule and arguments in a term. In we had to write our own "split" function in order to correctly handle nested terms. We still were only separating on commas or the left parenthesis (to pull a predicate from its arguments).

Some of our infix operators are now more than a single character, such as "<=" or "is". A small adaptation using the variable "lsep" (line 18) which stands for "length of separator" handles this.

Some infix operators are only allowed at the top level of a term. A new function "splitInfix" (line 35) and called from "Term" init (line 42) looks for infix operators (the list "infixOps" is far from complete) and essentially makes the string "a<=b" equivalent to "<=(a,b)".

Finally we come to the execution of our new operators. Up to now the search function took a term from a rule and then searched for matches in the database of other rules. These new operators do not initiate a search. Instead they are simply evaluated (with possible side effects) and if they succeed the rule is continued with the next term. The code for this (lines 201 to 216) check for is", "cut", "fail", and generic functions like "<" all of which are found only at the top level of a term.

Other new operators like "+" exist in nested terms and are processed by the eval function (line 244). Each of these operators is handled by its own function which builds a new term from its arguments.

Cut and Fail

Consider the following piece of Prolog.

childOf(X,Y) :- parent(Y,X)
{'B': chris, 'A': jon}
{'B': maryann, 'A': jon}

Jon is the child of both parents so Prolog returns two answers. But if we want to only find a first answer we can do the following instead.

childOf(X,Y) :- parent(Y,X),cut
{'B': chris, 'A': jon}

Cut stops alternatives in the search and then succeeds. In this is accomplished by simply truncating the queue of alternatives (line 220).

Fail is almost the exact opposite. It stops the current rule, leaving any alternatives alone. "Cut" and "fail" are sometimes used together to declare complete failure of the search.

Searching with a Queue, instead of a Stack

You may or may not have noticed in function that the stack of goals became a queue. Other than the change of the variable name, the only difference is the "queue.insert(0,c)" instead of "stack.append(c)" (line 224 in

The effect of this change is subtle, but interesting. It changes the tree search from depth-first to breadth-first. That, in turn, means that multiple goals are processed in parallel rather than one goal being completed before another is started. This was also discussed in "Queues, Trees and Water Buckets". It opens the door to parallel processing but also creates problems, especially with the "cut" operator. Not only does the queue need to be emptied but processes running in parallel need to stop so as not to add any new goals to the queue afterwards. It's basically a synchronization problem. I have read, however, that most modern Prologs do use a breadth-first search.

Where from here?

This is as far as I intend taking the Prolog project, but it can certainly be extended further. I'm quite surprised so much could be done in about 260 lines of Python, including whitespace.

If you do extend the program I would enjoy hearing from you.

All of the code files can be found in

If you have comments or suggestions You can email me at mail me

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