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Problem Solving Methods and Computer-Aided Knowledge Acquisition Goals and Achievements: Tools applicable for construction of many systems Structured design.

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Presentation on theme: "Problem Solving Methods and Computer-Aided Knowledge Acquisition Goals and Achievements: Tools applicable for construction of many systems Structured design."— Presentation transcript:

1 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

2 Expertsystemen 10 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

3 Expertsystemen 10 3 Knowledge representation in Rules RULE as domain knowledge? IF X is a rabbit THEN X has four legs Describes 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

4 Expertsystemen 10 4 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

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

6 Expertsystemen 10 6 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

7 Expertsystemen 10 7 Classification by Hayes-Roth (1983) Ten categories of Expert Systems: Interpretationdescription from observation Predictionconsequences from events Diagnosisfaults from symptoms Designconfiguration from constraints Planningstep sequence from goal Monitoringdeviations from behavior Debuggingremedies from faults Repairremedies from faults Instructionmodule sequence from feedback Controlsteps from goals and observations of 10 systems ? the same? description of the future? configuration of steps? Diagnosis and treatment of illness called ignorance

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

9 Expertsystemen 10 9 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) System InputOutput If the solution space is an enumerable set: Problem is to determine in what category our instance belongs: CLASSIFICATION Student LecturesKnowledge Patient TreatmentLife exp. Reactor Bar controlPressure

10 Expertsystemen 10 10 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 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) DataSolution Abstract data Solution class

11 Expertsystemen 10 11 Heuristic and Hierarchical Classification? 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.

12 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

13 Expertsystemen 10 13 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

14 Expertsystemen 10 14 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 H1 H3 H5 H2 H4 S1 S2 S3 S4

15 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 H1S1 Pr(S1) Pr(H1) Pr(H1 -> S1)

16 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

17 Expertsystemen 10 17 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 DataSolution Abstract data Solution class Clancey’s heuristic classification: SCR DiaR HER

18 Expertsystemen 10 18 Knowledge Elicitation in MORE 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 Rule level Abstract level

19 Expertsystemen 10 19 Knowledge Elicitation Steps of MORE 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

20 Expertsystemen 10 20 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! Domain Knowledge Base Compilation Elicitation Feedback Rule Base Reason using cost of test and repair

21 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

22 Expertsystemen 10 22 Similar approaches 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

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