Knowledge Management for Computational Intelligence Systems

1. Dr. R. Weber College of Information Science & Technology Drexel University Knowledge Management for Computational Intelligence Systems

2. Outline • What knowledge management (KM)? • What computational intelligence systems (CI)? • Why would CI systems need KM? • CBKM framework • Why would it work? • MD • CBR • Example: applying the CBKM framework • Requirements • Conclusions • Future Work • Acknowledgements • References & Bibliography

3. What knowledge management (KM)? • KM can be understood within a wide umbrella of perspectives

4. What knowledge management (KM)? • What knowledge management (KM)? First: that is computerized! Knowledge tasks are Not performed by humans Knowledge tasks are performed by computer programs

5. What knowledge management (KM)? • What knowledge management (KM)? Second: that implements these knowledge tasks Weber & Kaplan 2003

6. What computational intelligence (CI) systems ? …use so-called CI methods (e.g. evolutionary, fuzzy, learning) … …to create solutions to problems… …that are in imprecise contexts and that are highly unstructured… e.g., modeling, prediction, clustering, classification, scheduling, optimization Computational intelligence systems…

7. Why would CI systems need KM? They are constantly making decisions Create a new solution to each problem Each decision is valuable to be learned

8. Why would CI systems need KM? If the CI system must deliver high assurance and If it operates in changing and dynamic environments To guarantee it operates as required, it has to learn, adapt, and evolve

9. Why would CI systems need KM? • To deal with imprecise problems and contexts: • Use data or input elements • Define architecture (e.g. neural networks) • Define parameters The more flexible, the more complex

10. Why would CI systems need KM? • Designers of computational intelligence methods rely on trial and error to define parameters • Trial and error improves with experience • KM methodological approach to learn from experience • Different problems may require solutions created from different sets of parameters

11. Why would CI systems need KM? Some systems include multiple CI methods Which CI method has better produced results with which kinds of inputs? To answer all these questions…..

12. CBKM framework CBKM: case-based knowledge management

13. Case-Based Knowledge Management Framework • Main module to learn from its own experiences: • Main Case Base (MCB) • Individual Case Bases (ICB) • Lessons-learned module to learn from external experiences • Main LL Base • Individual LL Bases

14. Case-Based Knowledge Management Framework Target System

15. Why would it work? 1. MD 2. CBR CBKM framework Based on: Monitored Distribution (MD) Case-based Reasoning (CBR)

16. Why would it work? 1. MD 2. CBR Monitored Distribution: Proactive distribution of knowledge artifacts Knowledge artifacts: lessons learned, alerts

17. Why would it work? 1. MD 2. CBR Direct MD capture processes reuse understand user distribute

18. Why would it work? 1. MD 2. CBR Indirect MD capture processes reuse understand distribute Target System

19. Why would it work? 1.Monitored Distribution Evaluation: Weber & Aha, 2003 no lessons with lessons variation NEO plan 32h48 18 % 39h50 total duration* casualties 24 % 11.48 8.69 among evacuees casualties among 6.57 30 % 9.41 friendly forces

20. Why would it work? 1.CBR CBR cycle by Aamodt, Plaza 1994 Aha, 1998

21. Why would it work? 1.CBR CBR is the number 1 methodology recommended to support knowledge management applications • W. Cheetham, A. Varma, K. Goebel, “Case-based reasoning at General Electric,” in Proceedings of the Fourteenth Annual Conference of the International Florida Artificial Intelligence Research Society, Menlo Park, CA: AAAI Press, 2001, pp. 93-97. • D.W. Aha, I. Becerra-Fernandez, F. Maurer and H. Muñoz-Avila, Eds. Exploring Synergies of Knowledge Management and Case-Based Reasoning: Papers from the AAAI 1999 Workshop (Tech. Rep. WS-99-10), Menlo Park, CA: AAAI Press, 1999. • I. Watson, “Knowledge management and case-based reasoning: a perfect match?” in Proc. of the Fourteenth Annual Conference of the International Florida Artificial Intelligence Research Society, I. Russel and J. Kolen, Eds. Menlo Park, CA: AAAI Press, 2001, pp. 118-122. • I.D. Watson, Applying knowledge management: techniques for building corporate memories, Amsterdam; Boston: Morgan Kaufmann, 2003. • K.-D. Althoff, A. Birk, G. von Wangenheim and C. Tautz, “Case-based reasoning for experimental software engineering,” in Case-Based Reasoning Technology - From Foundations to Applications, M. Lenz, B. Bartsch-Spörl, H.-D. Burkhard, and S. Wess, Eds. Springer Verlag: LNAI 1400, 1998, pp. 235-254. • R. Weber, D.W. Aha, and I. Becerra-Fernandez, “Intelligent Lessons Learned Systems,” International Journal of Expert Systems Research and Applications, 20, No. 1, pp. 17-34, 2001. • R. Weber and R. Kaplan, “Knowledge-based knowledge management,” in Innovations in Knowledge Engineering, R. Jain, A. Abraham, C. Faucher and B.J. van der Zwaag, Eds. Adelaide: Advanced Knowledge International Pty Ltd, 2003. • R. Weber and D.W. Aha, “Intelligent delivery of military lessons learned,” Decision Support Systems, 34(3), pp. 287-304, 2003. • D.W. Aha, R. Weber, H. Muñoz-Avila, L.A. Breslow, and K.M. Gupta, “Lesson distribution gap,” in Proceedings of IJCAI, Menlo Park, CA: AAAI Press, 2001, 2, pp. 987-992.

22. Why would it work? 1.CBR CBR is a well establish reasoning methodology With deployed applications in a great variety of domains

23. Example: applying the CBKM framework interfaces Example Target System: CI-Tool Artificialneuralnetwork Geneticalgorithm Infofuzzynetwork Compactset Data Mart

24. Example: applying the CBKM framework interfaces ICB ICB ICB ICB Main Case Base Lessons-Learned Module Example Target System: CI-Tool Artificialneuralnetwork CBKM Framework Geneticalgorithm Infofuzzynetwork Compactset Data Mart

25. Example: applying the CBKM framework How is the CBKM framework managing knowledge in the CI-Tool? By learning which CI-Tool method should be recommended to each new solution and By learning which parameter configuration may produce a quality result

26. Integration Requirements • General: • Are there tasks that can be improved? • Does system require high levels of assurance? • Does it operate in a changing and dynamic environment?

27. Integration Requirements • Specific to CI systems: • Employ one or more CI methods that can benefit from KM approach • Integration should occur during design • Unless system presents flexible architecture • Functions in “improvable” tasks need be listed • Functions have to accept input from CBKM

28. Conclusions (i) • The CBKM framework uncovers knowledge previously unavailable • One of the main challenges is its auto maintenance • 3 levels of maturity characterized by: • intense interference of knowledge engineers • some interference • no interference

29. Conclusions (ii) • The use of CBR brings along an enormous technical infrastructure • In the vast literature in CBR maintenance • Neural networks: cluster to find redundant and typical cases for deletion strategies • Genetic algorithms to maintain similarity measure • Fuzzy rules

30. Conclusions (iii) • CBKM will make use of the CI methods coded in the target system to help perform its own maintenance • No need to add yet more code to the system • Maximizes its effectiveness and efficiency • If integrated early in life cycle, CBKM can help testing target system

31. Future Work • Current submission to the 7th European Conference In Case-Based Reasoning • Conflicts between CBR and Knowledge Management • How to adjust a case base created from all executions of a CI system to fit the CBR paradigm to reason with cases • How to adjust cases to quality solutions to meet CI system goals • Co-authored with Duanqing Wu, Amie Souter

32. Acknowledgements • Mark Last • NISTP • This work is supported in part by the National Institute for Systems Test and Productivity at USF under the USA Space and Naval Warfare Systems Command grant no. N00039-02-C-3244, for 2130 032 L0, 2002.

33. References & Bibliography (i) Knowledge management • R. Weber and R. Kaplan, “Knowledge-based knowledge management,” in Innovations in Knowledge Engineering, R. Jain, A. Abraham, C. Faucher and B.J. van der Zwaag, Eds. Adelaide: Advanced Knowledge International Pty Ltd, 2003. • I. Watson, “Knowledge management and case-based reasoning: a perfect match?” in Proc. of the Fourteenth Annual Conference of the International Florida Artificial Intelligence Research Society, I. Russel and J. Kolen, Eds. Menlo Park, CA: AAAI Press, 2001, pp. 118-122.

34. References & Bibliography (ii) Computational Intelligence • W. Pedrycz, “Computational intelligence as an emerging paradigm of software engineering”, in Proceedings of the 14th international conference on Software engineering and knowledge engineering, New York, NY:ACM Press, 2002, pp. 7-14. • J.C. Bezdek, "Computational intelligence defined -- by everyone," in Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, O. Kaynak, L.A. Zadeh, B. Turksen, and I.J. Rudas, Eds. Berlin:Springer, 1998, pp.10-37. • J.G. Digalakis, and K.G. Margaritis, “An experimental study of benchmarking functions for genetic algorithms,” International Journal of Computer Mathematics, 79(4), pp. 403-416, 2002. • A. Kandel, P. Saraph and M. Last, Test Set Generation and Reduction with Artificial Neural Networks, in “Artificial Intelligence Methods in Software Testing”, M. Last, et. al. (Eds.), World Scientific, Singapore , 2004. • A. Abraham and B. Nath, “Hybrid heuristics for optimal design of neural nets,” in Proceedings of the Third International Conference on Recent Advances in Soft Computing, R. John and R. Birkenhead, Eds. Berlin: Springer Verlag, 2000, pp. 15-22.

35. References & Bibliography (iii) CBR • J. Kolodner, Case-Based Reasoning. Los Altos, CA: Morgan Kaufmann, 1993. • R. Weber and D.W. Aha, “Intelligent delivery of military lessons learned,” Decision Support Systems, 34(3), pp. 287-304, 2003. • D.W. Aha, R. Weber, H. Muñoz-Avila, L.A. Breslow, and K.M. Gupta, “Lesson distribution gap,” in Proceedings of IJCAI, Menlo Park, CA: AAAI Press, 2001, 2, pp. 987-992. • I. Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, San Francisco, California: Morgan Kaufmann Publishers, Inc., 1997. • D. Leake, Case-Based Reasoning: Experiences, Lessons, and Future Directions, Menlo Park, California: AAAI Press/The MIT Press, 1996. • A. Aamodt and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approaches,” Artificial Intelligence Communications, 7 (1), pp. 39-59, 1994. • W. Cheetham, A. Varma, K. Goebel, “Case-based reasoning at General Electric,” in Proceedings of the Fourteenth Annual Conference of the International Florida Artificial Intelligence Research Society, Menlo Park, CA: AAAI Press, 2001, pp. 93-97. • S. Slade, “Case-based reasoning: A research paradigm”. AI Magazine Spring 1991, pp. 42-55. • C. Riesbeck, and R. Schank, “Inside case-based reasoning”. 1989. Lawrence Erlbaum. • I.D. Watson, Applying knowledge management: techniques for building corporate memories, Amsterdam; Boston: Morgan Kaufmann, 2003.

36. References & Bibliography (iv) CBR Maintenance • R. Weber, D.W. Aha, and I. Becerra-Fernandez, “Intelligent Lessons Learned Systems,” International Journal of Expert Systems Research and Applications, 20, No. 1, pp. 17-34, 2001. • C.W. Holsapple and K.D. Joshi, “Organizational knowledge resources,” Decision Support Systems, 31, pp. 39-54, 2001. • D. B. Leake, B. Smyth, D. C. Wilson, Q. Yang, “Special issue on maintaining case-based reasoning systems,” Computational Intelligence, 17(2), pp.193-195, 2001. • B. Smyth, E. McKenna, “Competence models and the maintenance problem,” Computational Intelligenc, 17(2), pp. 235-249, 2001. • L. Portinale and P. Torasso, “Case-base maintenance in a multimodal reasoning system,” Computational Intelligence, 17(2), pp. 263-279, 2001. • S. Craw, J. Jarmulak and R. Rowe, “Maintaining retrieval knowledge in a case-base reasoning system,” Computational Intelligence, 17(2), pp. 346-363, 2001. • R. K. De and S.K. Pal, “A neuro-fuzzy method for selecting cases,” in Soft Computing in Case Based Reasoning, S.K. Pal, T.S. Dillon and D.S. Yeung, Eds. London: Springer Verlag, 2001, chapter 10. • S.C.K. Shiu, X.Z. Wang, and D.S. Yeung, “Neuro-fuzzy approach for maitaining case bases”, in Soft Computing in Case Based Reasoning, S.K. Pal, T.S. Dillon and D.S. Yeung, Eds. London: Springer Verlag, 2001, chapter 11. • I. Watson, “A case study of maintenance of a commercially fielded case-based reasoning system,” Computational Intelligence, 17(2), pp. 387-398, 2001.