Production Systems, Matching, and Expert Systems for Artificial Intelligence (CIS 479)

Rule Based System

weakness – expert system

  1. Require a lot of detailed knowledge
  2. Restrict knowledge domain
  3. Not all domain knowledge fits rule format
  4. Expert consensus must exist
  5. Knowledge acquisition is time consuming
  6. Truth maintenance is hard to maintain
  7. Forgetting bad facts is hard

Forward Chaining

Do this until problem is solved or no antecedents match

  1. Collect the rules whose antecedents are found in WM.
  2. If more than one rule matches use conflict resolution strategy to eliminate all but one
  3. Do actions indicated in by rule "fired"

Conflict Resolution Strategies

  1. Level of Specificity
  2. Maximum Specificity ( number of antecedents) match antecedents and choose the one with the most matches.

  3. Physically order the rules. By doing this we can have subsets and we may also have preconditions
  4. Order added to state description (WM)
  5. { recency ordering based on date }

  6. Recency Ordering for rules
  7. Data Ordering
  8. Context Limiting
  9. Execution Time
  10. Fire All Application Rules

Simulation handout

R1 / XCON was created to solve problems

Spent 2 years to create the expert system

Stages

  1. Check Order – missing/ mismatched pieces
  2. Layout Processor Cabinets
  3. Put boxes in input/output cabinets and components in boxes
  4. Put panels in input/output cabinets
  5. Layout floor plan
  6. Indicates Cabling

OPS 5

If context is layout and assign power supply then add appropriate power supply to order

Two kinds of Popular Role Based Systems

  1. Synthesis Systems
  2. R1/XCON

    Tends to be forward chaining

    Often data driven

    Often make use of breadth first search

    This looks at all facts before proceeding

  3. Analysis & Diagnostic

Tend to use backward chaining

MYCIN

Often goal driven

Often depthfirst search

Backward Chaining Algorithm

Given goal g

loop

new goal

apply rule R

consult user

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