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Problem Solving Methods and Computer-Aided Knowledge Acquisition Goals and Achievements: Tools applicable for construction of many systems Structured design and elicitation for single system Overview of this lecture • • • • • • Limitations of Rule-based knowledge representation Expert System classifications Classification tasks Example: Electronics repair knowledge MORE: classification knowledge elicitation Conclusions Expertsystemen 10 2 Knowledge representation in Rules • RULE as domain knowledge? • Strategic knowledge is often IF X is a rabbit represented implicitly in THEN X has four legs Conflict Resolution (PRESS Describes in fact how to infer lecture 5). a conclusion: operational. • Implicit representations: update / maintenance • Mix Support, Strategic, • Rule models and checkers Structural knowledge: (lecture 9): partial solution IF Radio is dead THEN Put voltmeter on Conclusion: Rules are battery • GOOD as a basis for inferencing • Context dependence: • POOR as a general IF pinguin THEN not fly knowledge representation IF bird THEN fly formalism Expertsystemen 10 3 Strategy in Facade NLU • Linguistic domain knowledge is partitioned in • synonyms, • idiomatic expressions, • negations, • discourse (sub) acts • Per class same treatment (strategy) • id.expr: retract words • synonyms: high salience • This structure of knowledge is also linguistic knowledge! • Elicitation: talk to expert in familiar terms Expertsystemen 10 4 Knowledge compilation Tool System • Rules: the inferencing “assembly language” • Maintain knowledge at more abstract level • Compile knowledge into inferencing rules Domain Knowledge Base Compilation Elicitation Rule Base Consultation Inferencer Expertsystemen 10 5 General Knowledge Compilation Tools? • Design of tool is tightly linked with roles of knowledge and expert’s approach • Attempt: classify all possible expert systems into small number of categories • Ideal: make one tool per category • Expert Systems are too much different! • End with one compilation tool per system Expertsystemen 10 6 Classification by Hayes-Roth (1983) of 10 systems ? Ten categories of • Interpretation • Prediction Diagnosis and • Diagnosis treatment of • Design illness called •ignorance Planning • Monitoring • Debugging • Repair • Instruction • Control description of the future? Expert Systems: description from observation consequences from events faults from symptoms configuration configuration from constraints of steps? step sequence from goal deviations from behavior remedies from faults the same? remedies from faults module sequence from feedback steps from goals and observations Expertsystemen 10 7 Clancey: Interpretation and Construction tasks Interpretation • Task involving some working system • Solution from enumerable set • Top-down inference Construction • Task of formation of a working system • Solution space implicitly defined • Bottom-up inference Construction: • Different Problem Solving methods: Backtracking, Propose-and-Apply, Propose-and-Revise, Least-commitment … • RIME/XCON, VT/SALT .. • Lecture 14 Applies to subtasks Expertsystemen 10 8 Interpretation System as input output map • Input unknown: Control (what treatment is the best) • System unknown: Identify (what component is failed) • Output unknown: Predict (will the reactor explode) Input System Output Lectures Student Knowledge Treatment Patient Life exp. Bar control Reactor Pressure If the solution space is an enumerable set: Problem is to determine in what category our instance belongs: CLASSIFICATION Expertsystemen 10 9 Heuristic Classification method Clancey’s three steps: • Data Abstraction: 20.6Volt: “Low voltage” • Heuristic Match: Low Voltage indicates Power Supply problem • Solution Refinement: Continue within limited search space Abstract data Solution class Data Solution Systems with classification as main or sub task: • MYCIN: match data to preenumerated disease using rules with CF • SACON: suggest simulation type for MARC software • SOPHIE: Find faulty module in circuit, faulty component in module (measurements) • COMPASS: diagnose telephone switch (error messages) Expertsystemen 10 10 Heuristic and Hierarchical Classification? Clancey 1985, Heuristic • Choices may lead to overlapping subspaces • Difficult choices can be postponed Chandrasekaran 1986, Hierarchical • Strict taxonomy of solutions: no overlap • Need confirmation of each step because no correction possible • Choose bird if it flies, correct bat later • Choose bird if it flies, lays eggs and has feathers and bones. Expertsystemen 10 11 Repair Knowledge and Repair Strategies How to repair a circuit? • Repair shop?? • 200 electrical components • one or more faulty • Knowledge about properties of each component • Knowledge about interaction Strategy 1: • Test/replace each component in some order Strategy 2: • Employ structural grouping of components Expertsystemen 10 12 Grouping of system components Planning/Analysis phase: • Distinguish logical subunits of circuitry • Characterise behavior that differentiates between faults in subunits • For each subunit, list normal values for measurements • For each measurement, give components to determine it Consultation: • Run behavioral tests until faulty subunit is found • Measure in faulty unit • For deviating measurements, check suspect components • Replace defective component • Repeat until radio plays Domain independent Problem Solving Strategy that can be coded into Elicitation Tool Expertsystemen 10 13 MORE Domain Models • Hypotheses We want to select from one of the things that can be wrong • Symptoms Selection is based on these observations (attributes) • Conditions Influences on the likelyhood of hypothesis and symptoms • Tests Find out if a condition arises Expertsystemen 10 H1 H2 H3 S1 S2 S3 H4 H5 S4 14 Confidence Factors, Measure of (Dis) Belief • MORE generates Diagnostic Rules for H1 Hypo – Symp associations: IF S1 THEN H1 WITH (mb, md) • Diagnostic rule: MB Positive and MD Negative Confidence Factor • MB is high if • H1 is only/most likely explanation for S1 • Prior probability for S1 is low S1 Pr(S1) Pr(H1 -> S1) Pr(H1) • MD is high if • S1 is a very likely consequence of H1 • XS based on CF, not probability Expertsystemen 10 15 Conditions and Tests MORE Background conditions: • “Condensator problems are more likely if the radio was stored humid” • “Resistor problems are more likely if the radio was badly ventilated” MORE Tests: • Humid storage gives moisture patches • Bad ventilation overheats rectifier and output Expertsystemen 10 16 Symptom and Hypothesis Rules Symptom Confidence Rule: • Rank importance of observed syptoms • Use prior probability and background conditions • Use reliability induced by tests Hypothesis Expectancy Rule: • Rank probabilities of hypotheses • Use prior probabilities and background conditions Expertsystemen 10 Clancey’s heuristic classification: Abstract data Solution class Data Solution SCR DiaR HER 17 Knowledge Elicitation in MORE Long before MORE: • Give me a Rule … • I’ll add it to the program • Test exhaustively MORE • Tell • Tell • Tell Before MORE: • Give me a Rule • I’ll check if it looks familiar • I’ll add it to the program • Test • I’ll ask you questions until I think I know enough • I’ll convert the knowledge to rules for you Rule level Knowledge elicitation: me the Hypotheses me their probabilities me about Symptoms Abstract level Expertsystemen 10 18 Knowledge Elicitation Steps of MORE Questions that MORE may ask the Expert: Questions are guided by MORE’s state of the model: • Differentiation: What S differentiates between H1 and H2? • Apply when: H1 and H2 have no Differentiating Symptom • Frequency Conditionalization: • Apply when: What BC influences the S has no rules with high mb probability of S? and md • Symptom distinction: Refine S to distinguish H1 from H2 • Apply when: S has no rules with high mb Expertsystemen 10 19 MORE: Knowledge driven knowledge elicitation • MORE was good for building MUD; • otherwise insufficently general! Domain Knowledge Base Compilation • MORE contains problem solving knowledge • MORE collects domain knowledge from the Human Expert • MORE compiles PSM plus Domain knowledge into rules • MORE uses PSM knowledge to guide elicitation Rule Base Elicitation Feedback • Reason using cost of test and repair Expertsystemen 10 20 MUD • Drilling fluid used in oil excavation • Lubrication, cooling, waste removal, information stream • Drill interruptions are costly • Carefully continuously examine mud temperature, viscosity, composition • MUD was developed for the quick treatment of mud problems • MORE was developed for the quick treatment of MUD problems Expertsystemen 10 21 Similar approaches Construction systems: Interpretation systems: • VT and SALT: • PUFF and CENTAUR: Propose and Revise Hierarchical Hypothesize and Test (w/o single fault assumption, • XCON and RIME: resembles construction) Propose and Apply • TEST and TDE: Lectures 14 (and 15) Abstract HHaT in tree of hypotheses Expertsystemen 10 22