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Chapter 9: Rules and Expert Systems Lora Streeter

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Chapter 9: Rules and Expert Systems Lora Streeter. Rules for Knowledge Representation. IF...THEN... rules can be used to represent knowledge about objects IF A THEN B or A → B For example: IF name is “Bob” AND weather is cold THEN tell Bob \'Wear a coat\'.

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slide2

Rules for Knowledge Representation

IF...THEN... rules can be used to represent knowledge about objects

IF A THEN B

or

A → B

For example:

IF name is “Bob”

AND weather is cold

THEN tell Bob \'Wear a coat\'

slide3

Rule-Based (or Production) Systems

  • Provide recommendations or diagnoses
  • Determine a course of action in a particular situation
  • Solve a particular problem
  • Consists of:
    • A database of rules (aka knowledge base)
    • A database of facts
    • An interpreter, or inference engine
slide4

Rule Based Systems

  • Knowledge base = set of rules that represent the system\'s knowledge
  • Database of facts = inputs to the system that are used to derive conclusions or to cause actions
  • Interpreter = controls the process of arriving at conclusions
slide5

Forward Chaining

  • aka data-driven reasoning
    • Start reasoning from a set of data and ends up at the goal
  • Check facts in the fact database and compare to rule database
  • When they match up, that rule is triggered, and its\' conclusion added to the facts database
slide6

Forward Chaining Example

  • Rule 1: IF on first floor and button is pressed on first floor, THEN open door
  • Rule 2: IF on first floor AND button is pressed on second floor, THEN go to second floor
  • Rule 3: IF on first floor AND button is pressed on third floor, THEN go to third floor
  • Rule 4: IF on second floor AND button is pressed on first floor AND already going to third floor, THEN remember to go to first floor later.
slide7

Forward Chaining Example

  • We start with these facts in our database:
    • Fact 1: At first floor
    • Fact 2: Button pressed on third floor
    • Fact 3: Today is Tuesday
  • The system see that this matches Rule 3 (IF on first floor AND button is pressed on third floor, THEN go to third floor)
  • The conclusion, “Go to third floor” is added to the database of facts
  • Fact 3 matches none of our rules and is ignored
slide8

Forward Chaining Example

  • Later in the day, our facts database now contains the following information:
    • Fact 1: At first floor
    • Fact 2: Button pressed on second floor
    • Fact 3: Button pressed on third floor
  • Both rules 2 and 3 are triggered! We need to use some conflict resolution to sort this out
slide9

Conflict Resolution

  • Consider these rules:
    • IF it is cold, THEN wear a coat
    • IF it is cold, THEN stay at home
    • IF it is cold, THEN turn on the heat
  • Now if we have one fact in our database:
    • It is cold.
  • Clearly three possible outcomes can be derived
  • All three conclusions could easily be followed, but...
slide10

Conflict Resolution

  • ...many times conclusions are incompatible!
    • e.g. when prescribing medicines to patients
  • One solution: rules are given priority levels
  • If a conflict arises, highest priority wins
    • IF patient has pain,THEN prescribe painkillers priority 10
    • IF patient has chest pain, THEN treat for heart disease priority 100
slide11

Conflict Resolution

  • Another method is the longest-matching strategy
    • IF patient has pain, THEN prescribe painkiller
    • IF patient has chest pain AND patient is over 60 AND patient has history of heart conditions, THEN take to emergency room
  • The more specific match would be the rule that fires
  • A further method is to fire the rule that has matched the facts most recently added to the database.
slide12

Chaining

  • Forward chaining applies rules/facts to deduce whatever conclusions can be derived
    • Don\'t know what conclusions you\'re trying to prove
    • Can be inefficient by proving conclusions that aren\'t interesting
  • If we\'re trying to prove a single specific conclusion, backward chaining is more appropriate
slide13

Backward Chaining

  • Start from a conclusion (hypothesis) we want to prove
  • Show how it can be reached with rules and facts in the database
  • The conclusion we\'re aiming for is called a goal
    • Reasoning in this way is known as goal-driven reasoning
slide14

Forward vs. Backward Chaining

  • Rule 1: A ^ B → C
  • Rule 2: A → D
  • Rule 3: C ^ D → E
  • Rule 4: B ^ E ^ F → G
  • Rule 5: A ^ E → H
  • Rule 6: D ^ E ^ H → I

As for conflict resolution, fire the rules in the order they appear in the database

Fact 1: A

Fact 2: B

Fact 3: F

Goal: Prove H

slide15

Forward Chaining

  • Rule 1: A^B → C
  • Rule 2: A → D
  • Rule 3: C^D → E
  • Rule 4: B^E^F → G
  • Rule 5: A^E → H
  • Rule 6: D^E^H → I
  • Facts: A, B, F
  • Goal: H
slide16

Backward Chaining

  • Rule 1: A^B → C
  • Rule 2: A → D
  • Rule 3: C^D → E
  • Rule 4: B^E^F → G
  • Rule 5: A^E → H
  • Rule 6: D^E^H → I
  • Facts: A, B, F
  • Goal: H
slide17

Forward vs Backward Chaining

  • Used in many situations
    • A set of facts is available
    • Conclusion is not already known
    • Many possible conclusions

Few (or even just one) conclusion

Many possible facts

Not very many are necessarily relevant to the conclusion

slide18

Rule-Based Expert Systems

  • Model the behavior of an expert in some field
  • Designed to use the same rules that the expert would use to draw conclusions
  • Involve a number of people
    • End-user: the person who needs the system
    • Knowledge engineer: designer of rules
    • Domain expert: explain to the knowledge engineer how they go about diagnosing the problems
slide19

Architecture of an Expert System

  • A typical expert system architecture
slide20

Architecture of an Expert System

  • Knowledge base has domain specific knowledge represented by rules
  • Explanation system shows how the inference engine arrived at its conclusions
    • Essential if the advice is of critical nature, such as with a medical diagnosis system
  • Fact database has case-specific data
  • Inference engine uses the rules and facts to derive conclusions
  • Knowledge base editor used to update the information contained in the system
slide21

The Rete Algorithm

  • Problems with an expert system?
    • Lots of comparisons between rules and facts in the database
  • Solution?
    • The Rete Algorithm, an efficient method for solving impractical rule/fact comparisons
slide22

The Rete Algorithm

  • Is a directed, acyclic, rooted graph
    • aka a search tree
  • Each path from the root node to a leaf in the tree represents the left-hand side of the rule
    • Stores details of which facts have been matched by rules at that point
  • As facts are changed, the new facts are propagated down, changing node info accordingly
    • May add new fact, change info about an old fact, or delete an old fact
slide23

The Rete Algorithm

  • Depends on the principle that when using forward chaining, the object values change relatively infrequently
    • Few changes have to be made to the tree
    • Not particularly efficient where objects are continually changing
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