expert systems expert systems are ai programs that solve a highly technical problem in some domain...

Expert Systems

Expert systems are AI programs that solve a highly technical problem in some domain

Normally a human expert is used for solving such problems.

An expert system encodes a human expert’s knowledge. Common areas:

medicine science: chemistry, biology engineering agriculture military finance.

1COSC 2P93 Prolog: Expert Systems

Expert systems

Prolog is an excellent language for implementing expert systems

1. declarative Prolog can denote expert rules (knowledge base)

2. Prolog’s default execution is an “inference” strategy.

(called “backward chaining”)

3. Can write meta-interpreters for inference, which can implement things like explanation (knowledge traces), as well as new logic inference strategies.

4. Can use operators and grammars to make user-friendly knowledge languages.


Expert Systems: terms Knowledge-based system (or expert system): a program which

exhibits, within a specific domain, a degree of expertise in problem solving that is comparable with a human expert

expert: person with superior knowledge in some particular field, usually only obtained through experience

knowledge base: repository of expert's rules and facts about a domain

inference engine: procedure for drawing conclusions from knowledge base

knowledge engineer: develops, implements, and maintains a model of an expert's knowledge base

expert system shell: software used to implement an expert system; usually generic (and commercialized)


Expert System Architecture


KnowledgeBase

InferenceEngine

Interface

"Real world"

( humans, robots, machines, ... )

WorkingStorage

Simple example: Bird Identification

Expert’s rule

IF family is albatross and

color is white

THEN

bird is laysan_albatross

In Prolog the same rule is:

bird(laysan_albatross) :-

family(albatross),

color(white).


More Bird KB rules

bird(laysan_albatross):-

family(albatross),

color(white).

bird(black_footed_albatross):-

family(albatross),

color(dark).

bird(whistling_swan) :-

family(swan),

voice(muffled_musical_whistle).

bird(trumpeter_swan) :-

family(swan),

voice(loud_trumpeting).


Running Bird KB

At some point, the user must indicate the family and colour of a bird. In Prolog, these facts would be added to KB...

family(albatross).

color(dark).

Then...

?- bird(X).

X = black_footed_albatross


An expert system shell

Preferable to ask user to enter colour, or answer “yes” or “no” as necessary.

Also, don’t want to ask user same question repeatedly. Save answers (eg. colour).

But note that Prolog does not do this by default. Repeated calls to the same goal will be executed each time called. Need a “cache” of computed goals.

Improvements that a shell could offer:1. Add predicates to ask questions when required.

2. Save the answers to questions.


Shell

color(X) :- ask(color, X). % put this in KB.

ask(A, V):-known(yes, A, V), % succeed if true!. % and don’t ask user

ask(A, V):-known(_, A, V), % was asked before, but not “yes”!, fail. % therefore fail

ask(A, V):-write(A:V), % ask userwrite('? : '), read(Y), % get the answerasserta(known(Y, A, V)), % remember itY == yes. % succeed or fail


Bird ES

?- bird(X).

nostrils : external_tubular? yes.

live : at_sea? yes.

bill : hooked? yes.

size : large? yes.

wings : long_narrow? yes.

color : white? yes.

X = laysan_albatross


Explanation

A valuable feature of expert systems is their ability to explain their line of reasoning.

Often users want to know WHY advice was given, in addition to the advice itself.

Explanation also a good way to debug KB.

eg.nostrils : external_tubular? why.

[nostrils(external_tubular), order(tubenose), family(albatross), bird(laysan_albatross)]

nostrils : external_tubular?


Explanation

Why: explain the line of reasoning for this question Goes from node UP to the root of the tree.

How: How was some advice derived? Goes from node DOWN the branch.

Why not: Why was some other advice not given?

If Prolog’s inference is used, then the above can be implemented with a meta-interpreter. Very similar to the one that kept the proof tree for boolean logic. Also similar to grammars that keep the parse tree.


Simple meta-interpreter

prove(true,_) :- !.

prove(menuask(X,Y,Z),Hist) :- menuask(X,Y,Z,Hist), !.

prove(ask(X,Y),Hist) :- ask(X,Y,Hist), !.

prove((Goal, Rest),Hist) :-

!,

prove(Goal, [Goal|Hist]),

prove(Rest, Hist).

prove(Goal,Hist) :-

clause(Goal,Body),

prove(Body,Hist).


Meta-interpreter

2nd argument of prove is the “explanation” list.

Every time a goal is called, it is added to list. represents the goals from a node up the tree to the root.

Explanation list passed to ask, menuask utilities. If user asks “why”, then the list can be written out. Best to write it out in pieces, in “english” format.


Improving the shell

Using “op”, can make nicer looking rules in KB.

rule 1

if nostrils is external_tubular and

live is at_sea and

bill is hooked

then order is tubenose cf 80.

rule 2

if feet is webbed and

bill is flat

then order is waterfowl cf 80.


Explanation

With nicer looking rules, you can make explanation and queries more English-like...

Are the nostrils external_tubular? why.

The nostrils are external_tubular is necessary

To show that the order is tubenose

To show that the family is albatross

To show that the bird is a laysan_albatross


Uncertainty

Previous rules had “CF 80” terms: Certainty Factor Expertise is often vague, rather than black and white.

eg. medical diagnoses: could be likelihoods of different diseases.

Doctors want to consider all possibilities. Expert systems with uncertainty will allow multiple

conclusions to be reached. an ordered list of conclusions (disease diagnoses) will be

generated at end of a session...Measles CF 80

Chicken Pox CF 75

Yellow Fever CF 45


Forward-chaining

Backward chaining: Prolog’s default inference hierarchical, top-down strategy

However, some problems are not top-down in nature. eg. building complex machines: often start bottom-up

Forward chaining: bottom-up reasoning strategy You start with low-level facts (requirements), and “fire” rules until a high-

level conclusion reached. Prolog easily lets you make a forward-chaining meta-interpreter

This is a new “logic programming language” paradigm. However, no longer a top-down “tree” for inference (like regular

Prolog). Instead, forward-chaining uses a “working storage” of facts.

facts are asserted/retracted during inference.


Forward-chaining rules

rule id1:

[1: has(X,hair)]

==>

[assert(isa(X,mammal)),

retract(all)].

rule id3:

[1: has(X,feathers)]

==>

[assert(isa(X,bird)),

retract(all)].


Forward-chaining interpreter

% the main inference loop, find a rule and try it. if it fired, say so

% and repeat the process. if not go back and try the next rule. when

% no rules succeed, stop the inference

go :-

call(rule ID: LHS ==> RHS),

try(LHS,RHS),

write('Rule fired '),write(ID),nl,

!,go.

go.


Conclusion

Expert systems: one of the major commercial success stories of AI (along with data mining, vision, object-oriented programming, ...)

tens of thousands of expert systems being used. If you qualify (or not) for a mortgage or credit card, an

expert system probably made the decision!

Prolog is commonly used as an expert system implementation language. Its ability to interface with databases, other languages, and the

WWW, makes it ideal for implementing ES software.


expert systems expert systems are ai programs that solve a highly technical problem in some domain...

Documents

human expert expert

expert systems expert

expert systems prolog

domain expert systems

experience expert

expert rules knowledge

expert system architecture

domain knowledge base