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Expert Systems I

Gerstner Laboratory for Intelligent Decision Making and Control. Expert Systems I. Michal Pěchouček. Expert System Functionality. replace human expert decision making when not available assist human expert when integrating various decisions provides an ES user with

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Expert Systems I

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  1. Gerstner Laboratory for Intelligent Decision Making and Control Expert Systems I Michal Pěchouček

  2. Expert System Functionality • replace human expert decision making when not available • assist human expert when integrating various decisions • provides an ES user with • an appropriate hypothesis • methodology for knowledge storage and reuse • border field to Knowledge Based Systems, Knowledge Management • knowledge intensive × connectionist • expert system – software systems simulating expert-like decision making while keeping knowledge separate from the reasoning mechanism

  3. Expert Systems Classification • Unlike classical problem solver (GPS, Theorist) Expert Systems are weak, less general, very case specific • Exert systems classification: • Interpretation • Prediction • Diagnostic • Design & Configuration • Planning • Monitoring • Repair & Debugging • Instruction • Control

  4. Underlying Philosophy • knowledge representation • production rules • logic • semantic networks • frames, scripts, objects • reasoning mechanism • knowledge-oriented reasoning • model-based reasoning • case-based reasonig

  5. knowledge base knowledge base editor user inference engine world model preceptors explanation subsystem explanation subsystem Expert System Architecture

  6. Rule-Based System • knowledge in the form of if condition then effect(production) rules • reasoning algorithm: (i) FR  detect(WM) (ii) R  select(FR) (iii) WM  apply R (iv) goto (i) • conflicts in FR: • first, last recently used, minimal WM change, priorities • incomplete WM – querying ES (art of logical and sensible querying) • examples – CLIPS (OPS/5), Prolog

  7. Rule-Based System Example here  fine not here  absent absent and not seen  at home absent and seen  in the building in the building  fine at home and not holiday  sick here and holiday  sick not here, in the building fine not here, not holiday sick ? here  no ? seen  no ? holiday  no sick ? here  yes fine ? here  yes ? holiday  yes sick

  8. Data-driven × Goal-driven here seen holiday data driven absent building home goal driven fine sick

  9. Data-driven × Goal-driven • goal driven (backward chaining) ~ blood diagnostic, theorem proving • limited number of goal hypothesis • data shall be acquired, complicated data about the object • less operators to start with at the goal rather than at the data • data driven (forward chaining) ~ configuration, interpretation, • reasonable set of input data • data are given at the initial state • huge set of possible hypothesis • taxonomy of rules, meta-rules, priorities, …

  10. Knowledge Representation in ES • Shallow Knowledge Models • rules, frames, logic, networks • first generation expert systems • Deep Knowledge Models • describes complete systems causality • second generation expert systems • Case Knowledge Models • specifies precedent in past decision making

  11. Model Based Reasoning • Sometimes it is either impossible or imprecise to describe the domain in terms of rules … • Here we use a predictive computational model of the domain object in order to represent more theoretical deep knowledge model • Model is based either on • quantitative reasoning (differential equations, …) • qualitative reasoning (emphasizes some properties while ignoring other) • Very much used for model diagnosis and intelligent tutoring

  12. Qualitative Reasoning • Qualitative Reasoning is based on symbolic computation aimed at modeling of behavior of physical systems • commonsense inference mechanisms • partial, incomplete or uncertain information • simple, tractable computation • declarative knowledge • QR Techniques: • Constrain based – Qualitative Simulation QSIM • Component based – Envision • Process based – QPT (Qualitative Process Theory)

  13. QSIM – A Constraints Based Approach • Qualitative system is described by parameters, domains and constraints (relations among parameters) • Qualitative simulation is thus only breath-first-search in the space of possible combination of values of the parameters • Qualitative behaviour is thus a path in the tree from the initial state to some leaf state • The structure of the system model is given in the form of qualitative equation consisting of constraints: • arithmetic – add(A,B,C),mult(A,B,C) • derivative – der(height, velocity) • monotonicity – M+(wrinkle,age) M-(hunger,consumption)

  14. QSIM – A Constraints Based Approach • Qualitative State of each parameter is a couple: {value,direction} where value can be either an interval or landmark value and direction may be inc (increasing), dec (increasing) or std (steady) • Qualitative Reasoning Procedure: (i) wm  initial state (ii) succ  find-successors of first(wm) (iii) succ  filter(succ) (iv) wm  wm – first(wm) + succ • Filtering: pairwise consistency, redundancy, cycles, termination condition, logical direction of change, qualitative magnitude change

  15. QSIM – A Pendulum Example • system description: der(v,a) and der(s,v) • domains: a = {min,min,0,0,0,max,max} • v = {min,min,0,0,0,max,max} • s = {0,0,max,max} a a v s

  16. Case Based Reasoning • part of the machine learning lecture • Algorithms: • problem attributes description • retrieval of previous case • solution modification • testing new solution • repairing failure or inclusion into the plan library • Utilized widely in law domain (Judge)

  17. Knowledge Evolution • Strong Update - result of application of the knowledge extraction process on the set E  S. • Weak Update- relevant bits of the inference knowledge-base re-computation

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