Knowledge Representation and Expert Systems

Knowledge RepresentationandExpert Systems by Sujan Pakala Stephen Brown

What are they? • Diagnose a problems in a given domain • Capable of justifying its actions and conclusions • Applications: • Medical diagnosis applications • Locating equipment failures • Interpreting measurement data

Structure of Expert System User Shell • Major Components: • Knowledge Base • Inference Engine • User Interface • The Shell contains the inference engine and the user interface. User Interface Inference Engine Knowledge Base

Expert System Features • Goal driven reasoning or backward chaining • Data driven reasoning or forward chaining • Coping with uncertainty • Data representation • User interface • Generating Explanations

If – then Rules • Most popular formalism for knowledge representation • Additional features - modularity - incrementability - modifiability - transparency • categorical vs. probabilistic knowledge • knowledge elicitation • Example – refer figure.

Knowledge base as an Inference Network Kitchen_dry Leak_in_bathroom Hall_wet Problem_in_kitchen Bathroom_dry Leak_in_kitchen Window_closed No_water_from_outside No_rain

Backward chaining • Follow a chain of rules backwards • Stating rules into knowledgebase: - as straightforward prolog rules – hall_wet. bathroom_dry. leak_in_bathroom :- hall_wet, kitchen_dry.

Contd.. Disadvantage - not suited for normal user - hence, not syntactically distinct • Better way: - use ‘if’, ‘then’ etc. as operators- :-op(800, fx, if) - and write the rules as- if hall_wet and kitchen_dry then leak_in_bathroom. • Major disadvantage: - user has to state all relevant info.

Forward chaining

Generating Explanations • Has the ability to explain its results. • Two types of explanations: • how the system reached a given conclusion • why the system is asking a question

Coping with Uncertainty • Much of the time, the final answer is not known with complete certainty. • We can model uncertainty by assigning some qualification or measure of belief factor. • In our knowledge base, we can add a certainty factor to our conclusions: if hall_wet and bathroom_dry then problem_in_kitchen : 0.9.

Example: • User specifies certainty estimates: given(hall_wet, 1). % Hall is wet given(bathroom_dry, 1). % Bathroom is dry given(kitchen_dry, 0). % Kitchen is not dry given(no_rain, 0.8). % Probably no rain, but not sure given(window_closed, 0). % Window not closed

Continue Example: An interpreter for rules with certainties: certainty(P, Cert) :- given(P, Cert). certainty(Cond1 and Cond2, Cert) :- certainty(Cond1, Cert1), certainty(Cond2, Cert2), minimum(Cert1, Cert2, Cert). certainty(Cond1 or Cond2, Cert) :- certainty(Cond1, Cert1), certainty(Cond2, Cert2), maximum(Cert1, Cert2, Cert). certainty(P, Cert) :- if Cond then P : C1, certainty(Cond, C2), Cert is C1 * C2. ?- certainty( leak_in_kitchen, C). C = 0.8 Obtained: hall is wet & bathroom is dryproblem in the kitchen : 0.9 Possibility of some rainno water from outside : 0.8 Leak in kitchen is min(.8, .9) = 0.8

Demo • http://www.visual-prolog.com/vipexamples/geni/pdcindex.htm

Questions? Summary • Expert System’s typical functions: • Solving problem in a given domain • Explaining the problem-solving process • Handling uncertainty and incomplete information

Knowledge Representation and Expert Systems