1. Artificial Intelligence MEI 2008/2009 Bruno Paulette

2. Index What is Case-Based Reasoning? Interaction between CBR and Nature Diferences between CBR and RBS Cased-Based Systems CBR cycle

3. Index Case-Based Reasoning Techniques Case representation Indexing Storage Retrieval Adaptation

4. What is Case-based Reasoning? Example: Bank A bank manager, based on information provided by clients, like monthly income, job status, and others study if the bank should grant a loan of a amount or money, for that he must know, if it is likely or not, that the client will repay the loan. The bank employee has access to previous loans and their outputs

5. What is Case-based Reasoning? Experiences Problems Experiences Good or Bad – but always important Why they are important? Move forward – Evolve Knowledge

6. Differences between CBR and RBS For building an rule based system, it is necessary first to know how to address the problem RBS aren’t capable of outputting a solution based on past experiences (they don’t have any memory) If a problem was solved in the past, in RBS it will have to be solved again

7. Differences between CBR and RBS We can adapt old problems to solve new ones Useful when we have a good set of resolved cases Knowledge representation is dynamic Representation of experiences Cases

8. How to apply CBR? Databases Problem Solution DATABASE Problem Description If we use databases we could store Problems descriptions along with Solutions in a Database

9. Problems with this representation However in Databases The problem may not present itself in the same manner that is stored in databases Databases make exact matching; CBR requires approximate matching - we want to be able to find SIMILAR problems.

10. Case-Based Cycle “A Case Based reasoner solves new problems by adapting solutions that were used to solve old problems” Riesbieck and Schank, 1989

11. Case-Based Cycle Problem Analyze Proposed Solution Confirmed Solution Case- Base Revise ReuseRetain Retrieve

12. Case-Based Reasoning Techniques Case Representation A case is a contextualized piece of knowledge A case represents a experience The cases are: Historical Developed by an expert in the domain

13. Case-Based Reasoning Techniques The problem and solution spaces

14. Case-Based Reasoning Techniques Indexing Indexes are used frequently by databases, to speedup the data retrieving CBR also use indexes to speedup retrieval

15. Case-Based Reasoning Techniques Ref: 1423 Patient Name: Peter Pan Address: Never Land Photo: Age: eternal child Sex: Male Weight: 40 kg Height: 1,50m Blood Type: “Red” } } Unindexed features Indexed features Information that can be used for retrieval Information that may provide contextual information of value to the user

16. Case-Based Reasoning Techniques Storage Important aspect in to design efficient CBR systems. Represents conceptual view that represents the case. Choose the indexes that characterize the case. The case-base organized in a structure that supports efficient search and retrieval methods.

17. Case-Based Reasoning Techniques Retrieval Two techniques are used for case retrieval Nearest-Neighbor Retrieval Inductive Retrieval

18. Case-Based Reasoning Techniques Example: Bank A bank manager, based on information provided by clients, like monthly income, job status, and others study if the bank should grant a loan of a amount or money, for that he must know, if it is likely or not, that the client will repay the loan. The bank employee has access to previous loans and their outputs

19. Nearest-Neighbor Retrieval Case Indexes Monthly income Monthly loan repayment Case Result Good loanBad loan Monthly Income Monthly loan repayment

20. Nearest-Neighbor Retrieval Monthly Income Monthly loan repayment Bad Loans Good Loans New Case

21. Nearest-Neighbor Retrieval Similarity A is the target case B is the source case n is the number of attributes in each case I is an individual attribute from 1 to n f is a similarity function from attribute i W is the importance weighting of attribute i

22. Inductive Retrieval The case base is analyzed An induction algorithm is used to build a decision tree This decision tree classifies (or indexes) the cases The most widely used induction algorithm is ID3

23. Inductive Retrieval Loan status Monthly Income Job StatusRepayment Case 1Good2,000Salaried 200 Case 2Very bad4,000Salaried 600 Case 3Very good5,000Waged 300 Case 4Bad1,500Salaried 200 Repayment < 4000 Income > 1500 Case 1 Job Status Case 3Case 2Case 4 Good LoansBad Loans YesNo Yes SalariedWaged

24. Case-Based Reasoning Techniques Adaptation Types: None Structural Adapts rules or formulas directly to the solution stored cases Derivational Reuses the rules or formulas that generated the original solution to produce a new solution to the current problem

25. Bibliography Watson, Cap. 1, 2; Presentations from the last years.

26. Questions & Doubts