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Problem Solving Methods and Computer-Aided Knowledge Acquisition

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

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Problem Solving Methods and Computer-Aided Knowledge Acquisition

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  1. Problem Solving Methods andComputer-Aided Knowledge Acquisition Goals and Achievements: Tools applicable for construction of many systems Structured design and elicitation for single system

  2. 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

  3. RULE as domain knowledge?IF X is a rabbit THEN X has four legsDescribes in fact how to infer a conclusion: operational. Mix Support, Strategic, Structural knowledge:IF Radio is dead THEN Put voltmeter on battery Context dependence:IF pinguin THEN not fly IF bird THEN fly Strategic knowledge is often represented implicitly in Conflict Resolution (PRESS lecture 5). Implicit representations: update / maintenance Rule models and checkers (lecture 9): partial solution Conclusion: Rules are GOOD as a basis for inferencing POOR as a general knowledge representation formalism Knowledge representation in Rules Expertsystemen 10

  4. 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 Strategy in Facade NLU Expertsystemen 10

  5. Rules: the inferencing “assembly language” Maintain knowledge at more abstract level Compile knowledge into inferencing rules Domain Knowledge Base Compilation Elicitation Tool System Knowledge compilation Rule Base Consultation Inferencer Expertsystemen 10

  6. 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 General Knowledge Compilation Tools? Expertsystemen 10

  7. Classification by Hayes-Roth (1983) of 10 systems? description of the future? Ten categories of Expert Systems: • Interpretation description from observation • Prediction consequences from events • Diagnosis faults from symptoms • Design configuration from constraints • Planning step sequence from goal • Monitoring deviations from behavior • Debugging remedies from faults • Repair remedies from faults • Instruction module sequence from feedback • Control steps from goals and observations Diagnosis and treatment of illness called ignorance configuration of steps? the same? Expertsystemen 10

  8. 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 Applies to subtasks Construction: Different Problem Solving methods: Backtracking, Propose-and-Apply, Propose-and-Revise, Least-commitment … RIME/XCON, VT/SALT .. Lecture 14 Clancey: Interpretation and Construction tasks Expertsystemen 10

  9. 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) System Student Patient Reactor Input Lectures Treatment Bar control Output Knowledge Life exp. Pressure Interpretation If the solution space is an enumerable set: Problem is to determine in what category our instance belongs:CLASSIFICATION Expertsystemen 10

  10. 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 Systems with classification as main or sub task: MYCIN: match data to pre-enumerated 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) Abstract data Solution class Data Solution Heuristic Classification method Expertsystemen 10

  11. Clancey 1985, Heuristic Choices may lead to overlapping subspaces Difficult choices can be postponed Choose bird if it flies, correct bat later Chandrasekaran 1986, Hierarchical Strict taxonomy of solutions: no overlap Need confirmation of each step because no correction possible Choose bird if it flies, lays eggs and has feathers and bones. Heuristic and Hierarchical Classification? Expertsystemen 10

  12. 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

  13. 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 Grouping of system components Domain independent Problem Solving Strategy that can be coded into Elicitation Tool Expertsystemen 10

  14. MORE Domain Models • HypothesesWe want to select from one of the things that can be wrong • SymptomsSelection is based on these observations (attributes) • ConditionsInfluences on the likelyhood of hypothesis and symptoms • TestsFind out if a condition arises H1 S1 H2 S2 H3 S3 H4 H5 S4 Expertsystemen 10

  15. Confidence Factors, Measure of (Dis) Belief • MORE generates Diagnostic Rules for 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 • MD is high if • S1 is a very likely consequence of H1 • XS based on CF, not probability H1 S1 Pr(S1) Pr(H1 -> S1) Pr(H1) Expertsystemen 10

  16. 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

  17. Clancey’s heuristic classification: SCR Abstract data Solution class DiaR HER Data Solution 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

  18. Long before MORE: Give me a Rule … I’ll add it to the program Test exhaustively Before MORE: Give me a Rule I’ll check if it looks familiar I’ll add it to the program Test MORE Knowledge elicitation: Tell me the Hypotheses Tell me their probabilities Tell me about Symptoms I’ll ask you questions until I think I know enough I’ll convert the knowledge to rules for you Knowledge Elicitation in MORE Rule level Abstract level Expertsystemen 10

  19. Questions that MORE may ask the Expert: Differentiation:What S differentiates between H1 and H2? Frequency Conditionalization:What BC influences the probability of S? Symptom distinction:Refine S to distinguish H1 from H2 Questions are guided by MORE’s state of the model: Apply when:H1 and H2 have no Differentiating Symptom Apply when:S has no rules with high mb and md Apply when:S has no rules with high mb Knowledge Elicitation Steps of MORE Expertsystemen 10

  20. Domain Knowledge Base Compilation Elicitation MORE: Knowledge driven knowledge elicitation • 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 • MORE was good for building MUD; • otherwise insufficently general! Feedback Rule Base • Reason using cost of test and repair Expertsystemen 10

  21. 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

  22. Construction systems: VT and SALT:Propose and Revise XCON and RIME:Propose and Apply Lectures 14 (and 15) Interpretation systems: PUFF and CENTAUR:Hierarchical Hypothesize and Test(w/o single fault assumption, resembles construction) TEST and TDE:Abstract HHaT in tree of hypotheses Similar approaches Expertsystemen 10

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