Prolog Source Code

Prolog Source Code

Prior to beginning the design of the rules, you should study a sample expert system for diagnosing car problems in order to define rules for diagnosing the appliance described by the user manual you have been given in class.

Here are some instructions for running the sample expert system:

  1. Download the expert shell (see also below): 
  2. Download the knowledge base containing the rules, and save it in the same folder as the expert shell (see also below):
  3. To run SWI-Prolog in Windows, start 'plwin.exe' or double-click the Prolog file in Explorer. Load the expert system shell and the cars knowledge base by typing:

    ?- consult(exshell).
    ?- consult(cars).

    Ignore warnings about 'Singleton variables'. For further information go to Prolog Resources).
  4. Start the program:

    ?- solve.

The program will ask you questions. Eventually the program succeeds (it comes up with a recommendation, prints a justification for this recommendation, and then prints 'Yes') or fails (cannot make a recommendation). If the program cannot make a recommendation, that means either the car is not broken, or the knowledge base does not have the information needed to diagnose the problem.

The Cars Database

% This is the sample automotive diagnostic knowledge base for use
% with the EXSHELL expert system shell.
% When running it, be sure to load it with the file containing 

% To start it, give PROLOG the goal:
%  solve.

% Knowledge Base for simple automotive diagnostic expert system.
% Top level goal, starts search.

rule((fix(Advice) :-
 (bad_component(X),fix(X, Advice))), 100).

% rules to infer bad component of the failed system:

rule((bad_component(starter) :- 

rule((bad_component(battery) :- 

rule((bad_component(timing) :- 
 (bad_system(ignition_system), not(tuned_recently))),80).

rule((bad_component(plugs) :- 

rule((bad_component(ignition_wires) :-
 not(plugs(dirty)), tuned_recently)),80).

% Rules to infer system that failed.

rule((bad_system(starter_system) :- 
 (not(car_starts), not(turns_over))),90).

rule((bad_system(ignition_system) :- 
 (not(car_starts), turns_over,gas_in_carb)),80).

rule((bad_system(ignition_system) :- 

rule((bad_system(ignition_system) :- 
 (car_starts, runs(dies),gas_in_carb)),60).

% Rules to make reccommendation for repairs.

rule(fix(starter, 'replace starter'),100).
rule(fix(battery, 'replace or recharge battery'),100).
rule(fix(timing, 'get the timing adjusted'),100).
rule(fix(plugs, 'replace spark plugs'),100).
rule(fix(ignition_wires, 'check ignition wires'),100).

% askable descriptions



The Expert Shell Database

% This is, an expert-system shell, written in Prolog.  It is a
% slight modification of the code at

%The diagnosis is initiated by the goal 'solve'.

solve :- solve(fix(X),CF), nl.

solve :- retractall(know(_,_)),
        write('There is insufficient knowledge to make a diagnosis'),

% The notation "solve/2" means "solve with 2 arguments".
% solve/2 succeeds with
% argument 1 bound to a goal proven true using the current knowledge base
% argument 2 bound to the confidence in that goal.
% solve/2 calls solve/4 with appropriate arguments.  After solve/4 has completed, 
% it writes the conclusions and prints a trace.
solve(Goal, CF) :- 
 solve(Goal, CF, [], 20),  % use 20 as the threshold for pruning rules
 write(Goal), write(' was concluded with certainty '), write(CF), nl,nl,
 build_proof(Goal, _, Proof),nl,
 write('The proof is '),nl,nl,
 write_proof(Proof, 0), nl,nl.

%solve/4 succeeds with
% argument 1 bound to a goal proven true using the current knowledge base
% argument 2 bound to the confidence in that goal.
% argument 3 bound to the current rule stack
% argument 4 bound to the threshold for pruning rules.
%solve/4 is the heart of exshell.  In this version, I have gone back to the
% simpler version.  It still has problems with negation, but I think that
% this is more a result of problems with the semantics of Stanford Certainty
% factors than a bug in the program.
% The pruning threshold will vary between 20 and -20, depending whether,
% we are trying to prove the current goal true or false.
% solve/4 handles conjunctive predicates, rules, user queries and negation.
% If a predicate cannot be solved using rules, it will call it as a PROLOG predicate.

% Case 1: truth value of goal is already known
solve(Goal, CF, _, Threshold) :- 
 known(Goal, CF),!,
 above_threshold(CF, Threshold).

% Case 2: negated goal
solve( not(Goal), CF, Rules, Threshold) :- !,
 invert_threshold(Threshold, New_threshold),
 solve(Goal, CF_goal, Rules, New_threshold),
 negate_cf(CF_goal, CF).

% Case 3: conjunctive goals
solve((Goal_1,Goal_2), CF, Rules, Threshold) :- !,
 solve(Goal_1, CF_1, Rules, Threshold), 
 above_threshold(CF_1, Threshold),
 solve(Goal_2, CF_2, Rules, Threshold), 
 above_threshold(CF_2, Threshold),
 and_cf(CF_1, CF_2, CF).

%Case 4: backchain on a rule in knowledge base 
solve(Goal, CF, Rules, Threshold) :-
 rule((Goal :- (Premise)), CF_rule), 
 solve(Premise, CF_premise, 
  [rule((Goal :- Premise), CF_rule)|Rules], Threshold),
 rule_cf(CF_rule, CF_premise, CF),
 above_threshold(CF, Threshold).

%Case 5: fact assertion in knowledge base
solve(Goal, CF, _, Threshold) :-
 rule(Goal, CF), 
 above_threshold(CF, Threshold).

% Case 6: ask user
solve(Goal, CF, Rules, Threshold) :-
 askuser(Goal, CF, Rules),!,
 assert(known(Goal, CF)),
 above_threshold(CF, Threshold).

% Case 7A: All else fails, see if goal can be solved in prolog.
% solve(Goal, 100, _, _) :-
% call(Goal).

% Certainty factor predicates.  Currently, these implement a variation of 
% the MYCIN certainty factor algebra.
% The certainty algebra may be changed by modifying these predicates.

% negate_cf/2
% argument 1 is a certainty factor
% argument 2 is the negation of that certainty factor
negate_cf(CF, Negated_CF) :-
 Negated_CF is -1 * CF.

% and_cf/3
% arguments 1 & 2 are certainty factors of conjoined predicates
% argument 3 is the certainty factor of the conjunction
and_cf(A, B, A) :- A =< B.
and_cf(A, B, B) :- B < A.

% argument 1 is the confidence factor given with a rule
% argument 2 is the confidence inferred for the premise
% argument 3 is the confidence inferred for the conclusion
rule_cf(CF_rule, CF_premise, CF) :- 
 CF is CF_rule * CF_premise/100.

% argument 1 is a certainty factor
% argument 2 is a threshold for pruning
% If the threshold, T, is positive, assume we are trying to prove the goal
% true.  Succeed if CF >= T.
% If T is negative, assume we are trying to prove the goal
% false.  Succeed if CF <= T.
above_threshold(CF, T) :-
 T >= 0, CF >= T.
above_threshold(CF, T) :-
 T < 0, CF =< T.

% argument 1 is a threshold
%  argument 2 is that threshold inverted to account for a negated goal.
% If we are trying to prove not(p), then we want to prove p false.
% Consequently, we should prune proofs of p if they cannot prove it
% false.  This is the role of threshold inversion.
invert_threshold(Threshold, New_threshold) :- 
 New_threshold is -1 * Threshold.

% Predicates to handle user interactions.  As is typical, these 
% constitute the greatest bulk of the program.
% askuser/3
% argument 1 is a goal whose truth is to be asked of the user.
% argument 2 is the confidence the user has in that goal
% argument 3 is the current rule stack (used for why queries).

% askuser prints the query, followed by a set of instructions.
% it reads the response and calls respond/4 to handle that response

askuser(Goal, CF, Rules) :-
 nl,write('Is it true that '), write(Goal), nl,
 write('? '),
 respond(Answer,Goal, CF, Rules).

% argument 1 is the user response
%  argument 2 is the goal presented to the user
% argument 3 is the CF obtained for that goal
% argument 4 is the current rule stack (used for why queries).
% The basic scheme of respond/4 is to examine the response and return
% the certainty for the goal if possible.
% If the response is a why query, how query, etc., it processes the query
% and then calls askuser to re prompt the user.

% Case 1: user enters a valid confidence factor.
respond(CF, _, CF, _) :-
 CF =< 100, CF >= -100.

% Case 2: user enters 'n' for no.  Return a confidence factor of -1.0 
respond(n, _, -100, _).

% Case 3: user enters 'y' for yes.  Return a confidence factor of 1.0 
respond(y, _, 100, _).

% Case 4: user enters a pattern that matches the goal.  This is useful if
% the goal has variables that need to be bound.  
respond(Goal, Goal, CF, _) :-
 write('Enter confidence in answer'), nl,

% Case 5: user enters a why query
respond(why, Goal, CF, [Rule|Rules]) :-
 askuser(Goal, CF, Rules).

respond(why, Goal, CF, []) :-
 write('Back to top of rule stack.'),
 askuser(Goal, CF, []).

% Case 6: User enters a how query.  Build and print a proof
respond(how(X), Goal, CF, Rules) :-
 build_proof(X, CF_X, Proof),!,
 write(X), write(' was concluded with certainty '), write(CF_X), nl,nl,
 write('The proof is '),nl,nl,
 write_proof(Proof, 0), nl,nl,
 askuser(Goal, CF, Rules).

% User enters how query, could not build proof
respond(how(X), Goal, CF, Rules):-
 write('The truth of '), write(X), nl,
 write('is not yet known.'), nl,
 askuser(Goal, CF, Rules).

% Case 7: User asks for the rules that conclude a certain predicate 
respond(rule(X), _, _, _) :-
 write('The following rules conclude about '), write(X),nl,nl,
 rule((X :- Premise), CF),
 write(rule((X :- Premise), CF)), nl,

respond(rule(_),Goal, CF, Rules) :-
 askuser(Goal, CF, Rules).

% Case 8: User asks for help.
respond(help, Goal, CF, Rules) :-
 askuser(Goal, CF, Rules).

%Case 9: User wants to stop running the program.
respond(halt,_, _, _) :- write(' Thanks for using the diagnosis system.'),
         nl, nl, abort.

%Case 10: Unrecognized input
respond(_, Goal,CF, Rules) :-
 write('Unrecognized response.'),nl,
 askuser(Goal, CF, Rules).

% argument 1 is the goal being traced.
% argument 2 is the CF of that goal
% argument 3 is the proof tree
% build_proof does not do threshold pruning, so it can show
% the proof for even goals that would not succeed.
build_proof(Goal, CF, ((Goal,CF) :- given)) :- 
 known(Goal, CF),!.

build_proof(not(Goal), CF, not(Proof)) :- !,
 build_proof(Goal, CF_goal, Proof),
 negate_cf(CF_goal, CF).

build_proof((Goal_1, Goal_2), CF, (Proof_1, Proof_2)) :- !,
 build_proof(Goal_1, CF_1, Proof_1),
 build_proof(Goal_2, CF_2, Proof_2),
 and_cf(CF_1, CF_2, CF).

build_proof(Goal, CF, ((Goal,CF) :- Proof)) :-
 rule((Goal :- (Premise)), CF_rule),
 build_proof(Premise, CF_premise, Proof),
 rule_cf(CF_rule, CF_premise, CF).

build_proof(Goal, CF, ((Goal, CF):- fact)) :-
 rule(Goal, CF).

% build_proof(Goal, 1, ((Goal, 1):- call)) :-
% call(Goal).

% write_proof/2
% argument 1 is a portion of a proof tree
% argument 2 is the depth of that portion (for indentation)
% writes out a proof tree in a readable format
write_proof(((Goal,CF) :- given), Level) :-
 write(Goal), write(' CF= '), write(CF), 
 write(' was given by the user'), nl,!.

write_proof(((Goal, CF):- fact), Level) :-
 write(Goal), write(' CF= '), write(CF), 
 write(' was a fact in the knowledge base'), nl,!.

write_proof(((Goal, CF):- call), Level) :-
 write(Goal), write(' CF= '), write(CF), 
 write(' was proven by a call to prolog'), nl,!.

write_proof(((Goal,CF) :- Proof), Level) :-
 write(Goal), write(' CF= '), write(CF), write(' :-'), nl,
 New_level is Level + 1,
 write_proof(Proof, New_level),!.

write_proof(not(Proof), Level) :-
 New_level is Level + 1,
 write_proof(Proof, New_level),!.

write_proof((Proof_1, Proof_2), Level) :-
 write_proof(Proof_1, Level),
 write_proof(Proof_2, Level),!.

% indent/1
% argument 1 is the number of units to indent
indent(I) :-
 write('     '),
 I_new is I - 1,

% Prints all options for user responses
print_instructions :-
        write('Hello.  Please answer my questions with one of the following:'), nl,
 write('    "y.", which means "yes" (confidence value 100).'), nl,
 write('    "n.", which means "no"  (confidence value -100).'), nl, 
 write('    A number followed by a period.  The number, which should be'), nl,
 write('    between -100 and 100, is your confidence in the truth of the query.'), nl,
 write('    "why." to get an explanation of why I ask this question.'),nl,
 write('    "how(X)." to find out how confident I am in concluding X,'),nl,
 write('             and how I reached that conclusion.'),nl,
 write('    "rule(X)." to display all rules that conclude about X.'),nl,
 write('    "halt." to terminate consultation.'),nl,
 write('    "help." to print this message.'), nl.

% write_rule/1
% argument 1 is a rule specification
% writes out the rule in a readable format
write_rule(rule((Goal :- (Premise)), CF)) :-
 write(Goal), write('if'), nl,
 write('CF = '), write(CF), nl.

write_rule(rule(Goal, CF)) :-
 write('CF = '), write(CF), nl.

% write_premise
% argument 1 is a rule premise
% writes it in a readable format.
write_premise((Premise_1, Premise_2)) :-
 !, write_premise(Premise_1),
write_premise(not(Premise)) :-
 !, write('     '), write(not),write(' '), write(Premise),nl.
write_premise(Premise) :-
 write('     '), write(Premise),nl.

% Utility Predicates.
retractall(X) :- retract(X), fail.
retractall(X) :- retract((X:-Y)), fail.