Rule-Based Expert Systems

1. Rule-Based Expert Systems

2. Expert Systems • Acknowledge that computers do not posses general knowledge (common sense) • Attempt to train computer in a “limited domain” • Experts have deep, often complex knowledge, but generally for a limited domain

3. Can A Computer Do What an Expert Does? • Limited domain • The system has a finite, and relatively small number of things it needs to “know” about. • The processing might be complex, but computers are good at that. • Can the expert knowledge be extracted? • Knowledge Engineers do the extracting • Experts provide their knowledge • Programmers encoded the knowledge into an “expert system.”

4. Some History • In the early to mid 1980’s the desire to build “expert systems” was very high in the US and elsewhere. • Japanese 5th generation computing project • AI researchers aggressively recruited by industry • Expert systems were considered by many to be “the next big thing.”

5. Overstatement of Capabilities • The results of several expert systems were oversold. • Prospector didn’t really discover millions of dollars worth of Molybdenum. • The originators of the system never made this claim, but despite their efforts to stop it, the story is repeated to this day.

6. Frequently Cited Examples of Expert Systems • MYCIN • Infectious blood disease diagnosis • Dendral • Analyzed mass-spectra (chemistry) • PROSPECTOR • Geological analysis • R1 • Configure a VAX computer

7. Expert knowledge can be difficult to extract • Experts often do not really know how they do things themselves. Although the expert can perform the tasks, he/she does not necessarily have access to the mechanism used. • Experts have reasons to be uncooperative in the process of disseminating their expertise. • Experts often disagree on both processes and conclusions. • The process might require judgment that is not easily codified.

8. Rule-based Expert Systems • Sets of IF-THEN rules are established to codify expert knowledge. • If <antecedent> then <consequent> • Antecedents can be combined using logical operators • If <a> and <b> or <c> then <consequent> • IF “3 enemy stones in a row” AND NOT “3 friendly stones in a row” Then “place a stone in the row with 3 enemy stones.”

9. Knowledge Engineers • Tasked with working with the expert to extract expertise and codify in a set of rules. • Has training in the development of expert systems, but not necessarily in the application domain. • Know the capabilities of the technology and knows how to apply it.

10. Expert System Shells • Separate the mechanisms for making inference from the rule base • Facilitate the entry of rules by non-programmers • Provide reuse for what would otherwise be redundant code across expert systems

11. Expert System Components • Inference Engine • Forward or Backward-chaining • Conflict resolution algorithms • Rule-base • IF-THEN rules • Database • Current state on which IF-THEN rules are applied. • Explanation Facilities • An important advantage rule-based expert systems hold over other types of AI

12. Inference Engines • Forward-chaining • Submit current data to all rules • Rules make conclusions, which in turn, generate new data • “Inference Chains” result from initial data and the data generated in conclusions. • Backward-chaining • Try to prove a conclusion by working backwards from ways to prove it.

13. Forward-chaining Example (A,B,E, and D are given) • If Y and D then Z • If X and B and E then Y • If A then X • If C then L • If L and M then N A X B Y Z L C D E Example from Negnevitsky

14. Backward Chaining • Prolog uses backward chaining • Work backward from the goal. • Check rules that can provided the desired goal.

15. Backward Chaining Example • If Y and D then Z • If X and B and E then Y • If A then X • If C then L • If L and M then N A X B Y Z B C E D D E

16. Forward or Backward Chaining? • What do experts use? • Are we trying to prove a particular hypothesis? • Backward chaining • Are we trying to find all possible conclusions? • Forward chaining • What does the rule set look like? • Could be either one or a combination of both.

17. Conflict Resolution • What happens when two rules provide conflicting conclusions? • If it has feathers then it is a bird • If it can’t fly then it is not a bird • What if has feathers, but can’t fly?

18. Conflict Resolution Methods • Use rule-order as an implied priority • The first rule to provide an answer is used. • Assign a priority to each rule, the rule with the higher priority is sustained. • Longest Matching Strategy uses the rule with the most specific information. • If it cannot fly and has feathers then it is a bird. • Certainty-based conflict resolution • Measures of certainty are provided for data and rules. Most certain rule is sustained.

19. Frame-based Expert Systems • Frame • Marvin Minsky (1975) • Frame-based Expert Systems utilize frames to encapsulate data and methods about an entity. • Frames are similar to objects, but the data types and processing methods are quite different.

20. Frames • A frame is a data structure with typical knowledge about a particular object or concept [Negnevitsky]. • Frame is a collection of attributes called “slots” • Example slots for a truck • Engine size • Number of wheels • Slots consists of attribute/value pairs called facets • Value/18 • Default Value/4 • Range/[3-18] • User Query/”Enter the number of wheels:”

21. Demons • Slots or facets can contain procedures that are executed with the data is accessed or changed • When_Changed demon is executed when new information is placed in a slot. • Might include forward chaining or backward chaining rules • When_Needed demon is executed when information is read from a slot • Might include code to read sensors or try to prove a goal

22. Inheritance • Frames can inherit from other frames. • Frame implicitly contains all the slots contained in the inherited frame unless the frame overrides the slot with its own definition. • Inheritance is established with the “IS-A” relationship • In frames, inheritance is principally used to provide default values, rather than structure and methods.

23. Other Frame Relationships • Aggregation (a-part-of) • An engine is a-part-of a car • A spark plug is a-part-of an engine • Association (Other semantic relationships) • Examples • Ownership (computer has-owner Joe) • Uses (dentist uses drill) • Location (Joe is-near theDesk)

24. No Limits on Relationships • Frame can employ multiple inheritance • Frame can have any number of relationships • Relationships can be of any type that is useful.

25. Interactions of Frames and Rules • Different frame-based systems use different mechanisms. • Rules are often invoked by demons. Some systems allow different rule sets to be applied to different frames

26. Why Use Frames? • In large systems, frames can provide the system the capability to find relevant information quickly. • Making inferences from the most relevant information can provide greater efficiency and allow searches to be constrained. • Relationships between frames can be provided at a relatively low cost.

27. Advantages of Expert Systems • Provide an explanation capability • What rules fired to provide the conclusion? • Why other conclusions were not made. • For simple domains, the rule-base might be simple and easy to verify and validate. • The system might use a method similar to what the expert uses. • Expert system shells provide a means to build simple systems without programming

28. Disadvantages of Expert Systems • When the number of rules is large, the effect of adding new rules can be difficult to assess. • Expert knowledge is not usually easily codified into rules. • Expert often lack access to their own analysis mechanisms. • Validation/Verification of large systems is very difficult. • Track record does not seem to contain many successes. Relatively high-risk to implement.