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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'.

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Chapter 9: Rules and Expert Systems Lora Streeter

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  1. Chapter 9: Rules and Expert Systems Lora Streeter

  2. 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'

  3. 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

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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...

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. Architecture of an Expert System • A typical expert system architecture

  20. 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

  21. 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

  22. 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

  23. 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

  24. Rules and Expert Systems The End

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