Introduction to Computational Intelligent Motivation Main umbrella: Natural Computing Computational options: Levels of Abstraction Definition: CI Basic Properties of CI CI Main Paradigms Examples of Natural phenomenas Computational Intelligence: Modeling Methodology Applications of CI Recommended References

1. An Introduction to
Computational Intelligent (CI)
Ismael A. Ali
Doctoral Seminar | Department of Computer Science
Kent State University | Spring 2014
iali1@kent.edu

3. Outline
 Motivation
 Main umbrella: Natural Computing
 Computational options: Levels of Abstraction
 Definition: CI
 Basic Properties of CI
 CI Main Paradigms
 Examples of Natural phenomenas
 Computational Intelligence: Modeling
Methodology
 Applications of CI
 Recommended References

4.  New world of computation:
 Mobility; computation in everyplace
 Dynamic; computation for everything
 Adaptation and improvement; computation in every-
environ
 Smartness: make cup of tea
 Uncertainty and Noise
 missing information
 Pattern recognition
 Needs for computation to survive:
 Think
 Adapt
 Sense
 Move

5. Why from nature?
- The processes are well done and successfully and
for years
- we see the “natural processes” as “information
processing systems”
- We are dealing with Interdisciplinary study

6. Main umbrella: Natural Computing

7. Do not confuse!
 Nature for computation: CI
 “Bio-computing”
 Computation for nature
 “Computational biology”
 Nature for engineering
 “Bio-inspired engineering”

8. Computational options:
Levels of Abstraction

9. Source: studyblue.com

10. Definition
 Computational intelligence (CI):
is a set of nature-inspired computational
methodologies and approaches
to address complex real-world problems to which
traditional approaches, i.e., first principles modeling
or explicit statistical modeling, are ineffective or
infeasible
wikipedi
a

11. Computational Intelligence
- Pseudonyms:
Natural Computation
Nature-Inspired Computing
Soft Computing
Adaptive Systems

12. AI vs. CI
CI is a specific subset of AI, but while AI focuses on
the outcome, CI focuses on the mechanism.
Deep Blue: highly optimized + extensive knowledge base

13. Basic Properties of CI
 Mobility
 Adaptability
 Complexity
 Dynamics
 Robustness
 Sustainability
 And more....

14. CI Main Paradigms:
 artificial neural networks
 evolutionary computation
 fuzzy logic
 swarm intelligence
 artificial immune systems
 ant algorithms
 bee algorithms
 and more to come!

15. Examples of Natural phenomenas
 Pattern recognition
 in different
 levels of abstraction

16. DNA

17. Neural Networks

18. Human Immune System

19. Swarm

20. Lets make an
Computational Intelligence
Algorithm !

21. Computational Intelligence
Modeling Methodology

22. Sample Modeling Methodology
Source: greensmith, DCA, PhD thesis

23. Applications of CI
• Air Traffic Control,
• Scheduling,
• Machine Learning,
• Pattern Recognition,
• Job Shop Scheduling,
• Earthquake Prediction
Systems,
• Market Forecasting,
• Data-Mining,
• User-Mining,
• Resource Allocation,
• Path Planning,
• System modeling,
• Network security

24. Recommended References
 Books

26. Recommended References
 Conferences
 IEEE Computational Intelligence Society:
http://cis.ieee.org/
 Courses
 CITS7212 Computational Intelligence: The
University of Western Australia
http://undergraduate.csse.uwa.edu.au/units/CITS7212/schedule.html

27. Go out to the nature and try made your own algorithm(s)!

29. Any Q?