knowledge management for computational intelligence systems dr. r. weber college of information...

Knowledge Management for Computational Intelligence

Systems

Dr. R. WeberCollege of Information Science & Technology

Drexel University

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1. What knowledge management (KM)?2. What computational intelligence systems (CI)?3. Why would CI systems need KM?4. CBKM framework5. Why would it work?

1. MD2. CBR

6. Example: applying the CBKM framework7. Requirements8. Conclusions9. Future Work10.Acknowledgements11.References & Bibliography


1. MD2. CBR

6. Example: applying the CBKM framework7. Requirements8. Conclusions9. Future Work10. Acknowledgements11. References & Bibliography


Systems

Click to edit Master titleOutline

1. What knowledge management (KM)?2. What computational intelligence systems

(CI)?3. Why would CI systems need KM?4. CBKM framework5. Why would it work?

1. MD2. CBR


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Click to edit Master titleWhat knowledge management

(KM)?• KM can be understood within a wide

umbrella of perspectives

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1. What knowledge management (KM)?First: that is computerized!

Knowledge tasks are Not performed by humans

Knowledge tasks are performed by computer programs

What knowledge management (KM)?

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Click to edit Master titleWhat knowledge management

(KM)?1. What knowledge management (KM)?

Weber & Kaplan 2003

Second:that implements these knowledge tasks

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Click to edit Master titleWhat 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…

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Click to edit Master titleWhy would CI systems need KM?

They are constantly making decisions

Create a new solution to each problem

Each decision is valuable to be learned

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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

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• 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

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• 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

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Some systems include multiple CI methods

Which CI method has better produced results with which kinds of inputs?

To answer all these questions…..

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Click to edit Master titleCBKM framework

CBKM: case-based knowledge management

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Click to edit Master titleCase-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

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Click to edit Master titleCase-Based Knowledge Management Framework

TargetSystem

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Click to edit Master titleWhy would it work? 1. MD 2. CBR

CBKM framework

Based on:

Monitored Distribution (MD)Case-based Reasoning (CBR)

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Click to edit Master titleWhy would it work? 1. MD 2. CBR

Monitored Distribution:

Proactive distribution of knowledge artifacts

Knowledge artifacts: lessons learned, alerts

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Click to edit Master titleWhy would it work? 1. MD 2.

CBRDirect

MD

processes

reu

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un

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user

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processes

reuse

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TargetSystem

Indirect MD

Why would it work? 1. MD 2. CBR

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NEO plantotal duration*

casualtiesamong evacuees

no lessons

39h50

11.48

with lessons variation

32h48

8.69

18 %

24 %

duration until medical assistance*

29h37 24h13 18 %

casualtiesamong enemies

3.08 3.14 -2 %

9.41 6.57 30 % casualties among friendly forces

Evaluation: Weber & Aha, 2003

Why would it work? 1. Monitored Distribution

NEO plantotal duration*

casualtiesamong evacuees

no lessons

39h50

11.48

with lessons variation

32h48

8.69

18 %

24 %

9.41 6.57 30 % casualtiesamong friendly forces

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Click to edit Master titleWhy would it work? 1. CBR

CBR cycle by Aamodt, Plaza

1994Aha, 1998

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Click to edit Master titleWhy would it work? 1. CBR CBR is the number 1 methodology recommended to support knowledge management applications1. 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.

2. 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.

3. 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.

4. I.D. Watson, Applying knowledge management: techniques for building corporate memories, Amsterdam; Boston: Morgan Kaufmann, 2003.

5. 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.

6. 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.

7. 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.

8. R. Weber and D.W. Aha, “Intelligent delivery of military lessons learned,” Decision Support Systems, 34(3), pp. 287-304, 2003.

9. 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.

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Click to edit Master titleWhy would it work? 1. CBR

CBR is a well establish reasoning methodology

With deployed applications in a great variety of domains

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Click to edit Master titleExample: applying the CBKM

framework

Example Target System: CI-Tool

Data Mart

Geneticalgorithm

Infofuzzy

network

Compactset

Artificialneural

network

interfaces

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framework

Example Target System: CI-Tool

Data Mart

Geneticalgorithm

Infofuzzy

network

Compactset

Artificialneural

network

CBKMFramework

Lessons-Learned Module

interfaces

Main Case Base

ICBICB ICB

ICB

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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

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Click to edit Master titleIntegration 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?

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Click to edit Master titleIntegration 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

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Click to edit Master titleConclusions (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

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Click to edit Master titleConclusions (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

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Click to edit Master titleConclusions (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

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Click to edit Master titleFuture 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

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Click to edit Master titleAcknowledgements

• 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.

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Click to edit Master titleReferences & 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.

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Click to edit Master titleReferences & 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.

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Click to edit Master titleReferences & 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.

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Click to edit Master titleReferences & 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.

knowledge management for computational intelligence systems dr. r. weber college of information...

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