Expert Systems I

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