1. CS62S: Expert Systems Based on: The Engineering of Knowledge-based Systems: Theory and Practice, A. J. Gonzalez and D. D. Dankel

2. Rule-based systems (part 2) Backward reasoning Starting with the goal, rules are searched to see if the facts can lead to a value for the goal.

3. Backward reasoning steps: 1. Form a stack of all goals. 2. Find all rules that support the first goal. 3. Examine each rule: a) If rule supports goal then remove goal from stack and go to step 2. b) If another rule is needed to satisfy the parameter of a rule’s premise then that is a subgoal and is placed on the stack c) Otherwise, ask the user for a value. If this value is not satisfactory, move on to the next rule. 4. When the stack is empty, end execution.

4. Rule-based architecture Inference network Nodes – facts, intermediate goals Rules connect the nodes All interconnections are known prior to execution

5. Pattern-matching systems Relationships are formed during execution The premise of the rules are patterns, and may include Wild cards: ? Single field, $ zero or more fields Logical operators: ~ NOT, | OR Relational operators: Arithmetic operators

6. Traditionally pattern-matching is used with forward chaining and inference net for backward chaining. However, either may be used with any reasoning method.

7. Rete Algorithm pattern-network Tree formed from the premise in the rules. Root is the first item in the premise. join network. Connects the tree nodes and compares similarly named variable for consistency in values

8. Knowledge-based systems lifecycle Waterfall model used in software engineering: Problem analysis Requirements specification Design Implementation Testing Maintenance

9. Problem analysis Requirements specification Preliminary design Initial prototype Detailed design Knowledge acquisition Validation and verification Design adjustment Maintenance

10. Feasibility Analysis Is there a need? Suitable for expert systems techniques? Is the knowledge heuristic? Does it mimic human problem solving methods? Does the knowledge change periodically? Is the knowledge well understood by experts? Are the input data always correct and complete? Can conventional programming solve it? Does it pass the telephone test? Is an expert system really justified?

11. Feasibility Analysis (cont) Resources Management support Available expert Competent Articulate Close proximity