COMP 4200: Expert Systems

1. COMP 4200: Expert Systems Dr. Christel Kemke Department of Computer Science University of Manitoba

2. Reasoning in Expert Systems • knowledge representation in Expert Systems • shallow and deep reasoning • forward and backward reasoning • alternative inference methods • metaknowledge

3. Expert performance depends on expert knowledge! Experts and Expert Systems • Human Experts achieve high performance because of extensive knowledge concerning their field • Generally developed over many years

4. Types of Knowledge Knowledge Representationin XPScan include: • conceptual knowledge • terminology, domain-specific terms • derivative knowledge • conclusions between facts • causal connections • causal model of domain • procedural knowledge • guidelines for actions

5. Knowledge Modeling in XPS Knowledge Modeling Technique in XPS • mostly rule-based systems (RBS) • rule system models elements of knowledge formulated independently as rules • rule set is easy to expand • often only limited ‘deep’ knowledge, i.e. no explicit coherent causal or functional model of the domain

6. Shallow and Deep Reasoning • shallow reasoning • also called “experiential reasoning” • aims at describing aspects of the world heuristically • short inference chains • complex rules • deep reasoning • also called causal reasoning • aims at building a model that behaves like the “real thing” • long inference chains • simple rules that describe cause and effect relationships

7. Dilbert on Reasoning 1

8. Dilbert on Reasoning 2

9. Dilbert on Reasoning 3

10. General Technology of XPS Knowledge + Inference • core of XPS • Most often Rule-Based Systems (RBS) • other forms: Neural Networks, Case-Based Reasoning

11. Rule-Based Expert Systems Work with • a set of facts describing the current world state • a set of rules describing the expert knowledge • inference mechanisms for combining facts and rules in reasoning

12. Inference Engine Knowledge Base (rules) Working Memory (facts) Agenda Explanation Facility Knowledge Acquisition Facility User Interface

13. Architecture of Rule-Based XPS 1 Knowledge-Base / Rule-Base • stores expert knowledge as “condition-action-rules” (or: if-then- or premise-consequence-rules) • objects or frame structures are often used to represent concepts in the domain of expertise, e.g. “club” in the golf domain. Working Memory • stores initial facts and generated facts derived by the inference engine • additional parameters like the “degree of trust” in the truth of a fact or a rule ( certainty factors) or probabilistic measurements can be added

14. Architecture of Rule-Based XPS 2 Inference Engine • matches condition-part of rules against facts stored in Working Memory (pattern matching); • rules with satisfied condition are active rulesand areplaced on the agenda; • among the active rules on the agenda, one is selected (see conflict resolution, priorities of rules) as next rule for • execution (“firing”) – consequence of rule can add new facts to Working Memory, modify facts, retract facts, and more

15. Architecture of Rule-Based XPS 3 Inference Engine + additional components might be necessary for other functions, like • calculation of certainty values, • determination ofpriorities of rules • and conflict resolutionmechanisms, • a truth maintenance system(TMS) if reasoningwith defaultsandbeliefs is requested

16. Rule-Based Systems- Example ‘Grades’ - Rules to determine ‘grade’ • study  good_grade • not_study  bad_grade • sun_shines  go_out • go_out  not_study • stay_home  study • awful_weather  stay_home

17. Example ‘Grades’ Rule-Base to determine the ‘grade’: • study  good_grade • not_study  bad_grade • sun_shines  go_out • go_out  not_study • stay_home  study • awful_weather  stay_home Q1: If the weather is awful, do you get a good or bad grade? Q2: When do you get a good grade?

18. Forward and Backward Reasoning forward reasoning • Facts are given. What is the conclusion? A set of known facts is given (in WM); apply rules to derive new facts as conclusions (forward chaining of rules) until you come up with a requested final goal fact. backward reasoning • Hypothesis (goal) is given. Is it supported by facts? A hypothesis (goal fact) is given; try to derive it based on a set of given initial facts using sub-goals (backward chaining of rules) until goal is grounded in initial facts.

19. Example ‘Grades’ • study  good_grade • not_study  bad_grade • sun_shines  go_out • go_out  not_study • stay_home  study • awful_weather  stay_home forward reasoningrule chain given fact: awful_weather 6,5,1 backward reasoning hypothesis/goal: good_grade 1,5,6

20. Example ‘Grades’ – Reasoning Tree good grade bad grade study not study stay home go out awful weather sun shines

21. Example – Grades Working MemoryAgenda awful weather Rule 6 Select and apply Rule 6 awful weather stay home Rule 5 Select and applyRule 5

22. Example – Grades Working MemoryAgenda awful weather stay home study Rule 1 Select and applyRule 1 awful weather stay home study good grade empty DONE!

23. Example ‘Police’ – Reasoning Tree forward reasoning: Shield AND Pistol  Police backward reasoning: Police  Badge AND gun Police Bad Boy Badge AND Gun OR Revolver Pistol Shield Q: What if only ‘Gun’ is known?

24. Example ‘Police’ – Reasoning Tree Police Bad Boy Badge AND Gun OR Revolver Pistol Shield Q: What if only ‘Pistol’ is known as ground fact?

25. Example ‘Police’ – Reasoning Tree Bad Boy Police Badge AND Gun OR Shield Revolver Pistol Task: Write down the Rule-Base for this example!

26. Forward vs. Backward Chaining

27. Alternative Reasoning Methods • Theorem Proving • emphasis on mathematical proofs and correctness, not so much on performance and ease of use • Probabilistic Reasoning • integrates probabilities into the reasoning process • Certainty Factors • Express subjective assessment of truth of fact or rule • Fuzzy Reasoning • allows the use of vaguely defined predicates and rules

28. Metaknowledge • deals with “knowledge about knowledge” • e.g. reasoning about properties of knowledge representation schemes, or inference mechanisms • usually relies on higher order logic • in (first order) predicate logic, quantifiers are applied to variables • second-order predicate logic allows the use of quantifiers for function and predicate symbols • may result in substantial performance problems • CLIPS uses meta-knowledge to define itself, i.e. CLIPS constructs, classes, etc. - in a bootstrapping form