Chapter 9: Rules and Expert Systems
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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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Chapter 9: Rules and Expert Systems Lora Streeter

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Chapter 9 rules and expert systems lora streeter

Chapter 9: Rules and Expert Systems

Lora Streeter


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'


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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.


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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...


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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.


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

Architecture of an Expert System

  • A typical expert system architecture


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

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


Chapter 9 rules and expert systems lora streeter

Rules and Expert Systems

The End


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