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  1. 1. Expert Systems I Michal Pěchouček Gerstner Laboratory for Intelligent Decision Making and Control
  2. 2. Expert System Functionality <ul><li>replace human expert decision making when not available </li></ul><ul><li>assist human expert when integrating various decisions </li></ul><ul><li>provides an ES user with </li></ul><ul><ul><li>an appropriate hypothesis </li></ul></ul><ul><ul><li>methodology for knowledge storage and reuse </li></ul></ul><ul><li>border field to Knowledge Based Systems, Knowledge Management </li></ul><ul><li>knowledge intensive × connectionist </li></ul><ul><li>expert system – software systems simulating expert-like decision making while keeping knowledge separate from the reasoning mechanism </li></ul>
  3. 3. Expert Systems Classification <ul><li>Unlike classical problem solver (GPS, Theorist) Expert Systems are weak , less general, very case specific </li></ul><ul><li>Exert systems classification : </li></ul><ul><ul><li>Interpretation </li></ul></ul><ul><ul><li>Prediction </li></ul></ul><ul><ul><li>Diagnostic </li></ul></ul><ul><ul><li>Design & Configuration </li></ul></ul><ul><ul><li>Planning </li></ul></ul><ul><li>Monitoring </li></ul><ul><li>Repair & Debugging </li></ul><ul><li>Instruction </li></ul><ul><li>Control </li></ul>
  4. 4. Underlying Philosophy <ul><li>knowledge representation </li></ul><ul><ul><li>production rules </li></ul></ul><ul><ul><li>logic </li></ul></ul><ul><ul><li>semantic networks </li></ul></ul><ul><ul><li>frames, scripts, objects </li></ul></ul><ul><li>reasoning mechanism </li></ul><ul><ul><li>knowledge-oriented reasoning </li></ul></ul><ul><ul><li>model-based reasoning </li></ul></ul><ul><ul><li>case-based reasonig </li></ul></ul>
  5. 5. Expert System Architecture inference engine world model knowledge base user knowledge base editor preceptors explanation subsystem explanation subsystem
  6. 6. Rule-Based System <ul><li>knowledge in the form of if condition then effect (production) rules </li></ul><ul><li>reasoning algorithm: </li></ul><ul><ul><ul><ul><li>(i) FR  detect(WM) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>(ii) R  select(FR) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>(iii) WM  apply R </li></ul></ul></ul></ul><ul><ul><ul><ul><li>(iv) goto (i) </li></ul></ul></ul></ul><ul><li>conflicts in FR: </li></ul><ul><ul><li>first, last recently used, minimal WM change, priorities </li></ul></ul><ul><li>incomplete WM – querying ES (art of logical and sensible querying) </li></ul><ul><li>examples – CLIPS (OPS/5), Prolog </li></ul>
  7. 7. Rule-Based System Example <ul><li>here  fine </li></ul><ul><li>not here  absent </li></ul><ul><li>absent and not seen  at home </li></ul><ul><li>absent and seen  in the building </li></ul><ul><li>in the building  fine </li></ul><ul><li>at home and not holiday  sick </li></ul><ul><li>here and holiday  sick </li></ul><ul><li>not here, in the building  fine </li></ul><ul><li>not here, not holiday  sick </li></ul>? here  no ? seen  no ? holiday  no sick ? here  yes fine ? here  yes ? holiday  yes sick
  8. 8. Data-driven × Goal-driven here seen holiday absent building home fine sick data driven goal driven
  9. 9. Data-driven × Goal-driven <ul><li>goal driven ( backward chaining ) ~ blood diagnostic, theorem proving </li></ul><ul><ul><li>limited number of goal hypothesis </li></ul></ul><ul><ul><li>data shall be acquired, complicated data about the object </li></ul></ul><ul><ul><li>less operators to start with at the goal rather than at the data </li></ul></ul><ul><li>data driven ( forward chaining ) ~ configuration, interpretation, </li></ul><ul><ul><li>reasonable set of input data </li></ul></ul><ul><ul><li>data are given at the initial state </li></ul></ul><ul><ul><li>huge set of possible hypothesis </li></ul></ul><ul><li>taxonomy of rules, meta-rules, priorities, … </li></ul>
  10. 10. Knowledge Representation in ES <ul><li>Shallow Knowledge Models </li></ul><ul><ul><li>rules, frames, logic, networks </li></ul></ul><ul><ul><li>first generation expert systems </li></ul></ul><ul><li>Deep Knowledge Models </li></ul><ul><ul><li>describes complete systems causality </li></ul></ul><ul><ul><li>second generation expert systems </li></ul></ul><ul><li>Case Knowledge Models </li></ul><ul><ul><li>specifies precedent in past decision making </li></ul></ul>
  11. 11. Model Based Reasoning <ul><li>Sometimes it is either impossible or imprecise to describe the domain in terms of rules … </li></ul><ul><li>Here we use a predictive computational model of the domain object in order to represent more theoretical deep knowledge model </li></ul><ul><li>Model is based either on </li></ul><ul><ul><li>quantitative reasoning (differential equations, …) </li></ul></ul><ul><ul><li>qualitative reasoning (emphasizes some properties while ignoring other) </li></ul></ul><ul><li>Very much used for model diagnosis and intelligent tutoring </li></ul>
  12. 12. Qualitative Reasoning <ul><li>Qualitative Reasoning is based on symbolic computation aimed at modeling of behavior of physical systems </li></ul><ul><ul><li>commonsense inference mechanisms </li></ul></ul><ul><ul><li>partial, incomplete or uncertain information </li></ul></ul><ul><ul><li>simple, tractable computation </li></ul></ul><ul><ul><li>declarative knowledge </li></ul></ul><ul><li>QR Techniques: </li></ul><ul><ul><li>Constrain based – Qualitative Simulation QSIM </li></ul></ul><ul><ul><li>Component based – Envision </li></ul></ul><ul><ul><li>Process based – QPT (Qualitative Process Theory) </li></ul></ul>
  13. 13. QSIM – A Constraints Based Approach <ul><li>Qualitative system is described by parameters , domains and constraints (relations among parameters) </li></ul><ul><li>Qualitative simulation is thus only breath-first-search in the space of possible combination of values of the parameters </li></ul><ul><li>Qualitative behaviour is thus a path in the tree from the initial state to some leaf state </li></ul><ul><li>The structure of the system model is given in the form of qualitative equation consisting of constraints: </li></ul><ul><ul><li>arithmetic – add(A,B,C),mult(A,B,C) </li></ul></ul><ul><ul><li>derivative – der(height, velocity) </li></ul></ul><ul><ul><li>monotonicity – M+(wrinkle,age) M-(hunger,consumption) </li></ul></ul>
  14. 14. QSIM – A Constraints Based Approach <ul><li>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) </li></ul><ul><li>Qualitative Reasoning Procedure: </li></ul><ul><ul><ul><ul><li>(i) wm  initial state </li></ul></ul></ul></ul><ul><ul><ul><ul><li>(ii) succ  find-successors of first(wm) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>(iii) succ  filter(succ) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>(iv) wm  wm – first(wm) + succ </li></ul></ul></ul></ul><ul><li>Filtering : pairwise consistency, redundancy, cycles, termination condition, logical direction of change, qualitative magnitude change </li></ul>
  15. 15. QSIM – A Pendulum Example <ul><li>system description: der(v,a) and der(s,v) </li></ul><ul><li>domains: a = {min,  min,0  ,0,  0,max  ,max} </li></ul><ul><li>v = {min,  min,0  ,0,  0,max  ,max} </li></ul><ul><li>s = {0,  0,max  ,max} </li></ul>a a v s min,std 0,std max,std -,dec 0,std +,dec max,std a +,dec max,std +,inc 0,std v +,inc +,inc +,inc 0,std s min,std 0,std 0,std +,inc 0,std -,inc min,std a -,inc min,std -,dec 0,std v +,dec +,dec +,dec max,std s
  16. 16. Case Based Reasoning <ul><li>part of the machine learning lecture </li></ul><ul><li>Algorithms: </li></ul><ul><ul><li>problem attributes description </li></ul></ul><ul><ul><li>retrieval of previous case </li></ul></ul><ul><ul><li>solution modification </li></ul></ul><ul><ul><li>testing new solution </li></ul></ul><ul><ul><li>repairing failure or inclusion into the plan library </li></ul></ul><ul><li>Utilized widely in law domain (Judge) </li></ul>
  17. 17. Knowledge Evolution <ul><ul><li>Strong Update - result of application of the knowledge extraction process on the set E  S. </li></ul></ul><ul><ul><li>Weak Update - relevant bits of the inference knowledge-base re-computation </li></ul></ul>