Expert Systems in Prolog


An expert system emulates the decision-making ability of a human expert.

Prolog is very well suited for implementing expert systems due to several reasons:

Example: Animal identification

Our aim is to write an expert system that helps us identify animals.

Suppose we have already obtained the following knowledge about animals, which are rules of inference: These rules are not exhaustive, but they serve as a running example to illustrate a few points about expert systems.

The key idea of an expert system is to derive useful new information based on user-provided input. In the following, we see several ways to do this in Prolog.

Direct Prolog implementation

We now consider an implementation that uses Prolog rules directly to implement the mentioned inference rules.

This is straight-forward, using is_true/1 to emit a question and only proceeding with the current clause if the user input is the atom yes:
animal(dog)  :- is_true('has fur'), is_true('says woof').
animal(cat)  :- is_true('has fur'), is_true('says meow').
animal(duck) :- is_true('has feathers'), is_true('says quack').

is_true(Q) :-
        format("~w?\n", [Q]),
There is a clear drawback of this approach, which is shown in the following sample interaction:
?- animal(A).
has fur?
|: yes.
says woof?
|: no.
has fur?
|: yes.
says meow?
|: yes.

A = cat .
The system has asked a question redundantly: Ideally, the fact that the animal does have a fur would have to be stated at most once by the user.

How can we best implement this? It is tempting to mess with the global database somehow to store user input over backtracking. However, changing a global state destroys many elementary properties we expect from pure logical relations and is generally a very bad idea, so we don't do it this way.

Using a domain-specific language

To solve the shortcoming explained above, we will now change the representation of our rules from Prolog clauses to a custom language that we write and interpret a bit differently than plain Prolog. A language that is tailored for a specific application domain is aptly called a domain-specific language (DSL).

We shall use the following representation to represent the knowledge:
animals([animal(dog, [is_true('has fur'), is_true('says woof')]),
         animal(cat, [is_true('has fur'), is_true('says meow')]),
         animal(duck, [is_true('has feathers'), is_true('says quack')])]).
The inference rules are now represented by terms of the form animal(A, Conditions), by which we mean that A is identified if all Conditions are true. Note especially that using a list is a clean representation of conditions.

It is a straight-forward exercise to implement an interpreter for this new representation. For example, the following snippet behaves like the expert system we saw in the previous section, assuming is_true/1 is defined as before:
animal(A) :-
        member(animal(A,Cs), As),
        maplist(call, Cs).
Notably, this of course also shares the mentioned disadvantage:
?- animal(A).
has fur?
|: yes.
says woof?
|: no.
has fur?
Now the point: We can interpret these rules differently by simply changing the interpreter, while leaving the rules unchanged. For example, let us equip this expert system with a memory that records the facts that are already known because they were already entered by the user at some point during the interaction.

We implement this memory in a pure way, by threading through additional arguments that describe the relation between states of the memory before and after the user is queried for additional facts. For convenience, we are using DCG notation to carry around the state implicitly.

Here is an implementation that does this:
animal(A) :-
        Known0 = [],
        phrase(any_animal(Animals, A), [Known0], _).

any_animal([Animal|Animals], A) -->
        any_animal_(Animal, Animals, A).

any_animal_(animal(A0, []), Animals, A) -->
        (   { A0 = A }
        ;   any_animal(Animals, A)
any_animal_(animal(A0, [C|Cs]), Animals, A) -->
        state0_state(Known0, Known),
        { condition_truth(C, T, Known0, Known) },
        next_animal(T, animal(A0,Cs), Animals, A).

next_animal(yes, Animal, Animals, A)  --> any_animal([Animal|Animals], A).
next_animal(no, _, Animals, A)        --> any_animal(Animals, A).

state0_state(S0, S), [S] --> [S0].
It is only left to define condition_truth/4: Depending on what is already known, this predicate either uses the existing knowledge or queries the user for more information.

To distinguish these two cases in pure way, we use the meta-predicate if_/3:
condition_truth(is_true(Q), Answer, Known0, Known) :-
            Known0 = Known,
            ( format("~w?\n", [Q]),
              Known = [known(Q,Answer)|Known0])).

known_(What, Answer, Known, Truth) :-
        if_(memberd_t(known(What,yes), Known),
            ( Answer = yes, Truth = true ),
            if_(memberd_t(known(What,no), Known),
                ( Answer = no, Truth = true),
                Truth = false)).
And thus, at last, the question no longer appears redundantly:
?- animal(A).
has fur?
|: yes.
says woof?
|: no.
says meow?
|: yes.

A = cat .
Separating the knowledge base from the way it is interpreted has allowed us to add features while leaving the inference rules unchanged.

Using a different DSL

Consider now yet another way to solve the exact same problem. Let us view the animal identification task as interpreting the following decision diagram, where dotted lines indicate no, and plain lines indicate yes:
In this case, the diagram is in fact a full binary tree which can be represented naturally using Prolog terms. For example, let us represent the decision tree as follows, using a term of the form if_then_else/3 for each inner node, and animal/1 and false/0 for leaves:
tree(if_then_else('has fur',
                  if_then_else('says woof',
                               if_then_else('says meow',
                  if_then_else('has feathers',
                               if_then_else('says quack',
Other kinds of decision diagrams can also be represented efficiently with Prolog terms.

Such trees can be interpreted in a straight-forward way, using again the definition of is_true/1 to query the user:

animal(A) :-
        tree_animal(T, A).

tree_animal(animal(A), A).
tree_animal(if_then_else(Cond,Then,Else), A) :-
        (   is_true(Cond) ->
            tree_animal(Then, A)
        ;   tree_animal(Else, A)
Note: This fragment uses the impure if-then-else construct. This is logically sound only if the condition is sufficiently instantiated, so that its truth can be safely determined without prematurely committing to one branch.

Since each question appears at most once on every path from the root to a leaf, it is not necessary to keep track of which questions have already been answered:
?- animal(A).
has fur?
|: yes.
says woof?
|: no.
says meow?
|: yes.

A = cat.

Comparison of approaches

We have now seen three different ways to implement an expert system in Prolog: Each of these approaches was rather easy to implement in Prolog, and there are several other DSLs that would also be suitable. The question thus arises: Which DSL, if any, should we choose to implement expert systems in Prolog? Let us briefly consider the main points we have seen:
  1. Using Prolog directly is straight-forward. However, a naive implementation has a few drawbacks. In our case, the same question was unnecessarily asked repeatedly.
  2. Using a domain-specific language lets us cleanly separate the main logic of the expert system from additional features, such as keeping track of already answered questions.
  3. A DSL based on decision diagrams is very easy to interpret and automatically avoids redundant questions.
From these points alone, option (3) seems very attractive. However, it also raises a few important questions: First, how was the decision diagram even obtained, and does it faithfully model the conditions we want to express? It is rather easy to do it by hand in this example, but how would you do it in more complex cases? Second, how costly is the transformation from a rather straight-forward fact base as in option (2) to using decision diagrams instead? Third, is this really a good diagram, and what do we even mean by good? Are there orderings of nodes that let us reduce the number of questions? In the worst case, on average, in the best case? Fourth, how extensible is the language of decision diagrams? For example, can all animal identification tasks be modeled in this way? etc.

These questions show that the best choice depends on many factors.

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