Slides used for presenting the above mentioned paper at IJCAI-ECAI-18.

1. An Empirical Study of Knowledge Tradeoffs in Case-Based
Reasoning
Devi Ganesan and Sutanu Chakraborti
Department of Computer Science & Engineering
{gdevi, sutanuc}@cse.iitm.ac.in
Indian Institute of Technology Madras
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2. What is Case-Based Reasoning?
Case-Based Reasoning (CBR) is a problem solving paradigm that uses
past experiences to solve new problems.
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3. Experiences → Cases
Experiences are captured as Problem-Solution pairs
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4. Experiences → Cases
Experiences are captured as Problem-Solution pairs
Stored experiences are called Cases
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5. Knowledge Containers 1
in a Case-Based Reasoner
1
Richter, M.M. 1995. The knowledge contained in similarity measures. Invited talk at ICCBR-95, Sesimbra,4 / 29

6. Knowledge Containers 1
in a Case-Based Reasoner
Unlike most machine learning algorithms, CBR provides a framework for integrating domain
knowledge with data.
1
Richter, M.M. 1995. The knowledge contained in similarity measures. Invited talk at ICCBR-95, Sesimbra,4 / 29

7. Interplay
Knowledge containers are not independent of each other
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8. Interplay
Problem Solving Cycle
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9. Why is Interplay between Knowledge Containers Important?
Design
Maintenance
Learning and Evolution
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10. Our Contributions
We propose a novel measure to quantify the knowledge tradeoffs between
containers. The proposed measure is evaluated on synthetic and real-world datasets.
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11. Our Contributions
We propose a novel measure to quantify the knowledge tradeoffs between
containers. The proposed measure is evaluated on synthetic and real-world datasets.
The tradeoffs are visually illustrated using parallel coordinate plots.
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12. Our Contributions
We propose a novel measure to quantify the knowledge tradeoffs between
containers. The proposed measure is evaluated on synthetic and real-world datasets.
The tradeoffs are visually illustrated using parallel coordinate plots.
We have also studied the influence of the following two factors on knowledge tradeoffs:
Nature of underlying domain
User demands on solution quality
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13. Proposed Measure
For Quantifying Knowledge Tradeoffs
Footprint Size Reduction
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14. Footprint Set 2
Footprint set is a minimal set of cases that has the same problem solving
ability as the entire case base.
2
Smyth, Barry, and Elizabeth McKenna. ”Footprint-based retrieval.” ICCBR. Vol. 1650. 1999.
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15. Hypothesis 1
Knowledge contained in footprint set is as good as the knowledge
contained in entire case base.
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16. Hypothesis 1
Knowledge contained in footprint set is as good as the knowledge
contained in entire case base.
Hence, we use footprint size to quantify the knowledge contained in case
base.
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17. From CBR Literature
Iglezakis and Roth-Berghofer (2000) discuss the centrality of case base in maintenance
activities and one of their hypotheses is cases are natural crystallization points for the
knowledge in case-based reasoning systems.
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18. From CBR Literature
Iglezakis and Roth-Berghofer (2000) discuss the centrality of case base in maintenance
activities and one of their hypotheses is cases are natural crystallization points for the
knowledge in case-based reasoning systems.
Further, Smyth and McKenna (1998) argue that competence group is a fundamental unit of
competence in a case base.
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19. From CBR Literature
Iglezakis and Roth-Berghofer (2000) discuss the centrality of case base in maintenance
activities and one of their hypotheses is cases are natural crystallization points for the
knowledge in case-based reasoning systems.
Further, Smyth and McKenna (1998) argue that competence group is a fundamental unit of
competence in a case base.
These works support our way of quantifying the knowledge contained in case base by footprint
size.
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20. Hypothesis 2
Any addition of novel knowledge to the Vocabulary/Similarity/Adaptation container will lead
to a reduction in the size of footprint set
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21. Motivation for Hypothesis 2
Solves function in footprint algorithm connects the four containers.
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22. Motivation for Hypothesis 2
Solves function in footprint algorithm connects the four containers.
A case c is said to solve a target problem t iff c can be retrieved and adapted
to solve the target problem.
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23. Motivation for Hypothesis 2
Solves function in footprint algorithm connects the four containers.
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24. Hypotheses 1 and 2 motivate the idea of using reduction in footprint size
as a measure of quantifying the knowledge tradeoffs between containers.
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25. Formulation
Let V , CB, S, R represent the Vocabulary, Case Base, Similarity and Adaptation
containers and |FP(V ,CB,S,R)| be the size of footprint set.
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26. Formulation
Let V , CB, S, R represent the Vocabulary, Case Base, Similarity and Adaptation
containers and |FP(V ,CB,S,R)| be the size of footprint set.
The knowledge added by changing V to V , S to S and R to R can be quantified as
below.
∆Knowledge ≈ |FP(V ,CB,S,R)| − |FP(V ,CB,S ,R )| (1)
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27. Illustration of Tradeoffs on Synthetic Dataset
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28. Illustration of Tradeoffs on Synthetic Dataset
Underlying Domain Theory : D = 6A + 3B + C.
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29. Visualization of Tradeoffs
Parallel Coordinates
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30. Visualization of Tradeoffs
Parallel Coordinates
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31. Knowledge Configurations
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32. Knowledge Configurations
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33. Visualization of Tradeoffs
Similarity versus Case Base
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34. Visualization of Tradeoffs
Similarity versus Case Base
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35. Visualization of Tradeoffs
Similarity versus Case Base
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36. Visualization of Tradeoffs
Similarity versus Case Base
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37. Visualization of Tradeoffs
Similarity versus Case Base
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38. Visualization of Tradeoffs
Similarity versus Case Base
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39. Visualization of Tradeoffs
Similarity versus Case Base
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40. Visualization of Tradeoffs
Similarity versus Case Base
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41. Visualization of Tradeoffs
Similarity versus Adaptation
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42. Visualization of Tradeoffs
Similarity versus Adaptation
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43. Visualization of Tradeoffs
Similarity versus Adaptation
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44. Visualization of Tradeoffs
Similarity versus Adaptation
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45. Real World Datasets
We have evaluated the proposed measure on many UCI datasets.
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46. Real World Datasets
We have evaluated the proposed measure on many UCI datasets.
Iris
Boston Housing
Auto-MPG
20 Newsgroup - Relpol and Hardware
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47. Characterization of Domains
CBR is intended to operate over ill-defined domains.
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48. Characterization of Domains
CBR is intended to operate over ill-defined domains.
But, the general CBR paradigm does not place any restrictions on modeling a wide
spectrum of domains.
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49. Characterization of Domains
CBR is intended to operate over ill-defined domains.
But, the general CBR paradigm does not place any restrictions on modeling a wide
spectrum of domains.
We have attempted a quantitative characterization of domains using the proposed
measure.
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50. Characterization of Domains
Simulation
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51. Characterization of Domains
Simulation
Benefit of domain knowledge is defined as the maximum reduction in footprint size.
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52. Characterization of Domains
Impact of User Demands
Results
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53. Impact of User Demands
Results
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54. Summary
Proposed a novel measure to quantify knowledge tradeoffs between CBR containers.
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55. Summary
Proposed a novel measure to quantify knowledge tradeoffs between CBR containers.
Visual illustration of tradeoffs using parallel coordinates.
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56. Summary
Proposed a novel measure to quantify knowledge tradeoffs between CBR containers.
Visual illustration of tradeoffs using parallel coordinates.
Studied the impact of underlying domain and user demands on knowledge tradeoffs.
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57. Summary
Proposed a novel measure to quantify knowledge tradeoffs between CBR containers.
Visual illustration of tradeoffs using parallel coordinates.
Studied the impact of underlying domain and user demands on knowledge tradeoffs.
We expect that our interpretation of footprint cases and knowledge transfers will streamline
and motivate new avenues of cross-container maintenance activities in CBR.
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58. If you find our work to be interesting, please visit our poster in Hall B
during coffee break...
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59. Acknowledgements
Travel Grant
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60. Thank you all for your attention!
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