1. 13: Inference Techniques
Reasoning with AI
Forward and Backward
Inference Tree
Frames
Model-Based
Case-Based
Explanation, Metaknowledge
Uncertainty

2. Reasoning in Artificial Intelligence
Knowledge must be processed (reasoned with)
Computer program accesses knowledge for inferencing
Inference engine
Rule interpreter (in rule-based systems)
Directs search through the knowledge base

3. Reasoning …….Intuition
Formal methods (logical deduction)
Heuristic reasoning (IF-THEN rules)
Focus--common sense related toward more or less specific goals
Divide and conquer
Parallelism
Representation
Analogy
Synergy
Serendipity (Luck)

4. Reasoning Methods Deductive Reasoning Inductive Reasoning
Analogical Reasoning
Formal Reasoning
Procedural (Numeric) Reasoning
Metalevel Reasoning

5. Reasoning with Logic Modus Ponens
If A, then B
[A AND (A  B)]  B
A and (A  B) are propositions in a knowledge base
Modus Tollens: when B is known to be false
Resolution: combines substitution, modus ponens, and other logical syllogisms

6. Forward Chaining Data driven
Flying from Denver to Tokyo
Flights leaving Denver – Destinations
Are any destinations Tokyo?
If not, from those non Tokyo dests, what flights leave?
Which of those go to Tokyo? ……

7. Turban Chapter 13 Inference Techniques ing with Rules: Forward and Backward Chaining
Rule: IF A (is true)
THEN B (is the case)
IF = Premise
THEN = Assertion (or conclusion)
Pattern Matching:
Is A true?
Has it even been set?
If not, how is it set?

8. Backward Chaining Goal Driven
Flying to Tokyo from Denver
What flights arrive in Tokyo
Do any originate in Denver
If not, for each origination, what flights end there?
And where do they originate (Do any originate in Denver) ….

9. Chaining – rule linking
Forward Chaining
We have a situation
Search rules for premises that match situation
Forward chain with conclusion(s) as premise(s)
Backward
We know the condition or goal
Rules with conclusions that match goal
What other rules have conclusions that match those rules’ premises?

10. The Inference Tree Schematic view of the inference process
Similar to a decision tree (Figure 13.3)
Inferencing: tree traversal
Advantage: Guide for the Why and How Explanations

11. Inferencing with Frames
Much more complicated than reasoning with rules
Slot provides for expectation-driven processing
Empty slots can be filled with data that confirm expectations
Look for confirmation of expectations
Often involves filling in slot values
Can use rules in frames
Hierarchical reasoning
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

12. Model-based Reasoning
Based on knowledge of structure and behavior of the devices the system is designed to understand
Especially useful in diagnosing difficult equipment problems
Can overcome some of the difficulties of rule-based ES
Systems include a (deep-knowledge) model of the device to be diagnosed that is then used to identify the cause(s) of the equipment's failure
Reasons from "first principles" (common sense)
Often combined with other representation and inferencing methods
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

13. Model-based Reasoning 2
Model-based ES tend to be "transportable”
Simulates the structure and function of the machinery being diagnosed
Models can be either mathematical or component
Necessary condition is the creation of a complete and accurate model of the system under study
Especially useful in real-time systems

14. Case-based Reasoning Process
History without theory
Situation -> action
Scripts
Situation
Features (indexes)

15. Case-based Reasoning Process (Figure 13.4)
Assign Indexes
Retrieve
Modify
Test
Assign and Store
Explain, Repair and Test
Types of Knowledge Structures (Ovals)
Indexing Rules
Case Memory
Similarity Metrics
Modification Rules
Repair Rules

16. When to use CBR Weak causal model Undefined aspects or terms
Contradictory rules

17. Explanation and Metaknowledge
Human experts justify and explain their actions
ES should also do so
Explanation: attempt by an ES to clarify reasoning, recommendations, other actions (asking a question)
Explanation facility (justifier)

18. Rule Tracing Technique
“Why” Provides a Chain of Reasoning
Good Explanation Facility is critical in large ES
Understanding depends on explanation
Explanation is essential in ES
Used for training

19. Two Basic Explanations
Why Explanations - Why is a fact requested?
How Explanations - To determine how a certain conclusion or recommendation was reached.
Some simple systems - only at the final conclusion
Most complex systems provide the chain of rules used to reach the conclusion
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20. Uncertainty
Next week…