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 5 th 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 then  If then  Antecedents can be combined using logical operators  If and or then  If and or then  IF “3 enemy stones in a row” AND NOT “3 friendly stones in a row” 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 D B X E Y Z Example from Negnevitsky C L

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 D B E C ZY D X B 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.