Artificial Intelligence: Case-based & Model-based Reasoning
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Case-based reasoning (CBR) involves storing and retrieving past cases to solve new problems by adapting previous solutions, heavily relying on the human information processing model. It consists of various components such as a case base, retriever, adapter, refiner, executor, and evaluator. In contrast, model-based reasoning focuses on diagnosing system discrepancies by comparing observed behavior against predicted behavior based on a structured model, typically used in engineering contexts.
Artificial Intelligence: Case-based & Model-based Reasoning
- 1. Case-based Reasoning (CBR) • Collection of a set of cases – Store a case in the Case Base • CBR is based on human information processing (HIP) model in some problem areas – Thinking about how human processes information – Try to remember previous case/recall similar cases & modify to fit a new situation • Examples: Law, diagnosis, strategic planning – CEO: How should we modify last year’s plan? • Human experts depend heavily on past experiences when solving new problems
- 2. Case-based Reasoning • A CBR emulates this HIP model and maintains a historical case • It retrieves cases relevant to the present problem situation from the case base and decides on the solution to the current problem on the basis of the outcomes from previous cases
- 3. CBR Components • A case-based ES consists of – a case base – a retriever – an adapter – a refiner – an executer – an evaluator – (See diagram on next slide)
- 4. CBR Components User Interface Request Relevant Adapter Evaluator Retriever Case Base New Cases (w/ Results) Executer Refiner Prior Cases Prior Result Draft Solution Refined Solution Solution Performance Cases w/o Results
- 5. Case-based Parts • A case base functions as a repository of prior cases • The cases are indexed ( a key as with a database) so that they can be quickly recalled when necessary • A case contains the general descriptions of old problems • In Knowledge Base: Set of Rules • In Case Base: Set of Cases – Creates extra difficulty in retrieving
- 6. Case-base Parts - The Retriever • When a new problem is entered into a case based system, a retriever decides on the features similar to the stored cases • Retrieval is done by using features of the new cases as indexes into the case base
- 7. Case-based Parts - The Adapter • An adapter examines the differences between these cases and the current problem • It then applies rules to modify the old solution to fit the new problem
- 8. Case-based Parts - The Refiner • A refiner critiques the adapted solution against prior outcomes • One way to do this is to compare it to similar solutions of prior cases • If a known failure exists for a derived solution, the system then decides whether the similarities is sufficient to suspect that the new solution will fail
- 9. Case-based Parts - The Executor • Once a solution is critiqued, an executer applies the refined solution to the current problem
- 10. Case-based Parts - The Evaluator • If the results are as expected, no further analysis is made, and the cases and its solution is stored for use in future problem solving • If not, the solution is repaired
- 11. Model-based Reasoning • A model-based system is based on a model of the structure and behavior of the device that the system is designed to simulate • Used for well structured problems – Not for stock pricing/modeling, not well structured – Engineering Problems • Ex: Diagnosing hardware or a machine • Ex: Automobile diagnostics • Based on written documentation • The problem is extracting knowledge
- 12. Model-based Reasoning • Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do) • The difference between them is called a discrepancy, indicating a defect • Then a process is initiated to diagnose the nature and location of the defect • Could be a mathematical equation
- 13. Model-based Reasoning Actual Device Model Predicted Behavior Observed Behavior Discrepancy
- 14. Model-based Reasoning • Correct Operation: • Assume we have a model-based system built to diagnose the following simple device with 3 multipliers and 2 adders • Once the logic is developed, executes quickly A = 3 B = 3 C = 2 D = 2 A = 3 Mult - 1 Mult - 2 Mult -3 Add - 3 Add - 3 F = 12 G = 12 3 2 3 2 2 3 6 6 6 6 6 6 6 12 12
- 15. Model-based Reasoning • Incorrect Operation: – Diagnostics A = 3 B = 3 C = 2 D = 2 A = 3 Mult - 1 Mult - 2 Mult -3 Add - 3 Add - 3 F = 10 G = 12 3 2 3 2 2 3 6 6 6 6 6 6 6 10 12 6 6 6 Culprit of problem? No - 6 comes out Culprit of problem? No - 6 comes out Culprit of problem? No - 6 comes out Culprit of problem? Yes - 10 comes out, problem with Carry Bit Predicate Logic can be used here