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EXPERT SYSTEMS AND KNOWLEDGE REPRESENTATION Ivan Bratko Faculty of Computer and Info. Sc. University of Ljubljana

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EPERT SYSTEMS An expert system behaves like an expert in some narrow area of expertise Typical examples: Diagnosis in an area of medicine Adjusting between economy class and business class seats for future flights Diagnosing paper problems in rotary printing Very fashionable area of AI in period

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SOME FAMOUS EXPERT SYSTEMS MYCIN (Shortliffe, Feigenbaum, late seventies) Diagnosis of infections PROSPECTOR (Buchanan, et al. ~1980) Ore and oil exploration INTERNIST (Popple, mid eighties) Internal medicine

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Structure of expert systems

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FEATURES OF EXPERT SYSTEMS Problem solving in the area of expertise Interaction with user during and after problem solving Relying heavily on domain knowledge Explanation: ability of explain results to user

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REPRESENTING KNOWLEDGE WITH IF-THEN RULES Traditionally the most popular form of knowledge representation in expert systems Examples of rules: if condition P then conclusion C if situation S then action A if conditions C1 and C2 hold then condition C does not hold

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DESIRABLE FEATURES OF RULES Modularity: each rule defines a relatively independent piece of knowledge Incrementability: new rules added (relatively independently) of other rules Support explanation Can represent uncertainty

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TYPICAL TYPES OF EXPLANATION How explanation Answers user’s questions of form: How did you reach this conclusion? Why explanation Answers users questions: Why do you need this information?

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RULES CAN ALSO HANDLE UNCERTAINTY If condition A then conclusion B with certainty F

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EXAMPLE RULE FROM MYCIN if 1 the infection is primary bacteremia, and 2 the site of the culture is one of the sterilesites, and 3 the suspected portal of entry of the organism is the gastrointestinal tract then there is suggestive evidence (0.7) that the identity of the organism is bacteroides.

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EXAMPLE RULES FROM AL/X Diagnosing equipment failure on oil platforms, Reiter 1980 if the pressure in V-01 reached relief valve lift pressure then the relief valve on V-01 has lifted [N = 0.005, S = 400] if NOT the pressure in V-01 reached relief valve lift pressure, and the relief valve on V-01 has lifted then the V-01 relief valve opened early (the set pressure has drifted) [N = 0.001, S = 2000]

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EXAMPLE RULE FROM AL3 Game playing, Bratko 1982 if 1 there is a hypothesis, H, that a plan P succeeds, and 2 there are two hypotheses, H1, that a plan R1 refutes plan P, and H2, that a plan R2 refutes plan P, and 3 there are facts: H1 is false, and H2 is false then 1 generate the hypothesis, H3, that the combined plan ‘R1 or R2' refutes plan P, and 2 generate the fact: H3 implies not(H)

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A TOY KNOWLEDGE BASE: WATER IN FLAT Flat floor plan Inference network

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IMPLEMENTING THE ‘LEAKS’ EXPERT SYSTEM Possible directly as Prolog rules: leak_in_bathroom :- hall_wet, kitchen_dry. Employ Prolog interpreter as inference engine Deficiences of this approach: Limited syntax Limited inference (Prolog’s backward chaining) Limited explanation

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TAILOR THE SYNTAX WITH OPERATORS Rules: if hall_wet and kitchen_dry then leak_in_bathroom. Facts: fact( hall_wet). fact( bathroom_dry). fact( window_closed).

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BACKWARD CHAINING RULE INTERPRETER is_true( P) :- fact( P). is_true( P) :- if Condition then P, % A relevant rule is_true( Condition). % whose condition is true is_true( P1 and P2) :- is_true( P1), is_true( P2). is_true( P1 or P2) :- is_true( P1) ; is_true( P2).

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A FORWARD CHAINING RULE INTERPRETER forward :- new_derived_fact( P), % A new fact !, write( 'Derived: '), write( P), nl, assert( fact( P)), forward % Continue ; write( 'No more facts'). % All facts derived

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FORWARD CHAINING INTERPRETER, CTD. new_derived_fact( Concl) :- if Cond then Concl, % A rule not fact( Concl), % Rule's conclusion not yet a fact composed_fact( Cond). % Condition true? composed_fact( Cond) :- fact( Cond). % Simple fact composed_fact( Cond1 and Cond2) :- composed_fact( Cond1), composed_fact( Cond2). % Both conjuncts true composed_fact( Cond1 or Cond2) :- composed_fact( Cond1) ; composed_fact( Cond2).

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FORWARD VS. BACKWARD CHAINING Inference chains connect various types of info.: data... goals evidence... hypotheses findings, observations... explanations, diagnoses manifestations... diagnoses, causes Backward chaining: “goal driven” Forward chaining: “data driven”

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FORWARD VS. BACKWARD CHAINING What is better? Checking a given hypothesis: backward more natural If there are many possible hypotheses: forward more natural (e.g. monitoring: data-driven) Often in expert reasoning: combination of both e.g. medical diagnosis water in flat: observe hall_wet, infer forward leak_in_bathroom, check backward kitchen_dry

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GENERATING EXPLANATION How explanation: How did you find this answer? Explanation = proof tree of final conclusion E.g.: System: There is leak in kitchen. User: How did you get this?

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% is_true( P, Proof) Proof is a proof that P is true :- op( 800, xfx, <=). is_true( P, P) :- fact( P). is_true( P, P <= CondProof) :- if Cond then P, is_true( Cond, CondProof). is_true( P1 and P2, Proof1 and Proof2) :- is_true( P1, Proof1), is_true( P2, Proof2). is_true( P1 or P2, Proof) :- is_true( P1, Proof) ; is_true( P2, Proof).

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WHY EXPLANTION Exploring “leak in kitchen”, system asks: Was there rain? User (not sure) asks: Why do you need this? System: To explore whether water came from outside Why explanation = chain of rules between current goal and original (main) goal

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UNCERTAINTY Propositions are qualified with: “certainty factors”, “degree of belief”, “subjective probability”,... Proposition: CertaintyFactor if Condition then Conclusion: Certainty

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A SIMPLE SCHEME FOR HANDLING UNCERTAINTY c( P1 and P2) = min( c(P1), c(P2)) c(P1 or P2) = max( c(P1), c(P2)) Rule “if P1 then P2: C” implies: c(P2) = c(P1) * C

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DIFFICULTIES IN HANDLING UNCERTAINTY In our simple scheme, for example: Let c(a) = 0.5, c(b) = 0, then c( a or b) = 0.5 Now, suppose that c(b) increases to 0.5 Still: c( a or b) = 0.5 This is counter intuitive! Proper probability calculus would fix this Problem with probability calculus: Needs conditional probabilities - too many?!?

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TWO SCHOOLS RE. UNCERTAINTY School 1: Probabilities impractical! Humans don’t think in terms of probability calculus anyway! School 2: Probabilities are mathematically sound and are the right way! Historical development of this debate: School 2 eventually prevailed - Bayesian nets, see e.g. Russell & Norvig 2003 (AI, Modern Approach), Bratko 2001 (Prolog Programming for AI)

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