1. Knowledge Engineering

2.  Process of acquiring knowledge from experts and building knowledge base  Narrow perspective  Knowledge acquisition, representation, validation, inference, maintenance  Broad perspective  Process of developing and maintaining intelligent system

3. KE Process  Knowledge representation  Organized knowledge  Acquisition of knowledge  General knowledge or metaknowledge  From experts, books, documents, sensors, files  Inferences  Software designed to pass statistical sample data to generalizations  Knowledge validation and verification  Explanation and justification capabilities

4. In rule-based expert system, the domain knowledge is represented by a set of IF-THEN production rules and data is represented by a set of facts about the current situation. The inference engine compares each rule stored in the knowledge base with facts contained in the database. Inference Process (1 of 4)

5. Inference Process (2 of 4) Done in three stages:  match  select  execute  Match : contents of the working memory are compared to the facts and rules contained in the knowledge base  Select: When consistent match found the corresponding rules are placed in the conflict set.  Execute: When all matched rules are placed in the conflict set one of the rules is selected for execution

6. Fact: A is XFact: B is y Rule: IF A is x THEN B is y Knowledge base Database MatchFire Figure : The inference engine cycles via a match-fire procedure Inference Process (3 of 4)

7.  The matching of the IF parts to the facts produces inference chains.  The inference engine must decide when the rules have to be fired. There are two principal ways in which rules are executed:  Forward Chaining  Backward Chaining Inference Process (4 of 4)

8.  It’s the data-driven reasoning.  The reasoning starts from the known data and proceeds forward with that data.  Each time only the topmost rule is executed.  When fired, the rule adds a new fact in the database.  Any rule can be executed only once.  The match-fire cycle stops when no further rules can be fired. Inference Process: Forward Chaining

9. Let’s see an example Inference Process: Forward Chaining

10. Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N Match Fire ABCDE X Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N Match Fire ABCDE LX Cycle #1 Inference Process: Forward Chaining

11. Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N Match Fire ABCDE YL Cycle #2 X Knowledge-Base Y & D  Z X & B & E  Y A  X C  L L & M  N Match Fire Database ABCDE ZYLX Cycle #3 Inference Process: Forward Chaining

12.  It’s the goal-driven reasoning.  Here an expert system has the goal and the inference engine attempts to find the evidence to prove it.  First the knowledge base is searched to find rules that might have the desired solution.  Such rules must have the goal in their THEN parts. If such rule is found and its IF part matches data in the database, then the rule is fired and the goal is proved. Inference Process: Backward Chaining

13. Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N ABCDE Pass 1: Goal: ZPass 2: Sub-goal: y Z Knowledge-Base Y & D  Z X & B & E  Y A  X C  L L & M  N Y Database ABCDE ? Inference Process: Backward Chaining

14. Pass 3: Sub goal:X Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N ABCDE X ? Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N ABCDE Pass 4: Sub goal:X Match Fire X Inference Process: Backward Chaining

15. Pass 5: Sub-goal: Y Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N ABCDE Pass 6:Goal: Z Match Fire YX Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N ABCDE Match Fire ZYX Inference Process: Backward Chaining

16. Knowledge-Base Database Y & D  Z X & B & E  Y A  X C  L L & M  N Match Fire ABCDE LX Inference Process: Backward Chaining YZ Pass 7:Goal: L

17. Forward vs. Backward Chaining Forward ChainingBackward Chaining planning, controldiagnosis data-drivengoal-driven (hypothesis) bottom-up reasoningtop-down reasoning find possible conclusions supp orted by given facts find facts that support a given hypothesis similar to breadth-first searchsimilar to depth-first search antecedents (LHS) control evaluation consequents (RHS) control evaluation

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