1. Case-Based Reasoning CBR Cycle CBR Problem Issues
Knowledge Representation
Case Retrival
Case Adaptation Strategies
Learning Mechanism

3. Knowledge Representation
The Dynamic Memory Model
Memory Organization Packets (MOP)
Norms (common features)
Cases
Indexes (to discriminate features…)
The Category & Exemplar Model
Categories
Examplars
Features
Network

5. Knowledge Representation
The Category & Exemplar Model
Categories
Exemplars
Features
Network

7. Case Retrieval Memory (Case-Base) Organization Search Strategies Match
Index Schemes
Match
Weight Matching Algorithm (Nearest Neighbors)

8. 1. Memory Organization and Retrieval Algorithm
Flat Memory, Serial Search
Shallow indexing
Partitioning of the case library
Parallel retrieval
Shared Feature Networks(Based on MOP), tree/graph
Provide a means of clustering cases so that cases that share many features are clustered together. (Clustering-Discrimination)

9. 1. Memory Organization and Retrieval Algorithm
Discrimination Networks
Discrimination first, Clustering second.
Index Scheme
Does a system need Index Scheme ?
How to organize index (Scheme)?
What kinds of features can be used as indexes (Discrimination)?

10. Nearest Neighbor Matching (Weight Matching) Algorithm
NumericEvaluation:
nk=1 Wk * Sim( fIk, fRk )
nk=1 Wk
Wk : Weight of feature k
Sim : similarity function of the primitives
fIk, fRk : the values for feature fk in the input and retrieved cases.

11. Adaptation Methods and Strategies
Simple Substitution
Parameter Adjustment
Global Constraint Satisfactions

12. Learning in CBR--Learning from Problem Solving
What can be learned ?
New experience (New case)
Strategies:
Storing cases in the case base