Course: Introduction to Computational Intelligence

Credits: 3 Prof.: Paulo Sergio S. Borges, Dr.

1. Introduction (6 class-hours)

1. 1.1. General View of the course: Presentention and discussion of its contents, goals, importance, limitations, grading system.
   1.2. What is Computational Intelligence; Computational Paradigms (CI), Artificial Intelligence (AI) e Biological Intelligence (BI).
   1.3 Historical Resume; development of the research and aplications of CI.

2. Problems associated with Computational Intelligence (15 class-hours)

1. 2.1. The limits of Computer Science: A computer with the size of the Universe.
   2.2. The Turing test; "The chinese Room"; "The Busy Beaver Game"; "The Halting Problem".
   2.3. Complexity and undecidebility; The works of Kurt Gödel and Gregory Chaitin and their relation with Computational Intelligence.
   2.4. Rules, Randomness and Caos; Expoents of Liapunov.
   2.5. Satisfability; Problems NP-Complete.

3. Models for Computational Intelligence (18 class-hours)

• 3.1. Specialist Systems
  3.2. Bayesian Networks
  3.3 Fuzzy Logic
  3.4 Analisys of Ranges
  3.5 Rough Sets
  3.6 Conexionism and Neural Networks.
  3.7 Evolutionary Computation
            3.7.1. Automatas celulars
            3.7.2. Evolutionary Programming
            3.7.3. Evolutionary Estrategies
            3.7.4. Genetic Algorithms
            3.7.5. Classificatory Systems
  3.8 Hibrid Systems

4. Other Topics related to Computational Intelligence (6 class-hours)

• 4.1 Teory of Caos.
  4.2 Some projects in the CI area: The programs SHRDLU, AMBR, ACME and COPYCAT.
  4.3 Cognitive Maps.
           4.3.1 Computational Simulation: The game "Life", the system "Tierra", etc.

Grading System

Consists in elaboring, throughout the course, an essay in paper form, which can be considered as publishable (under the professor's criteria), to be proposed by the student, with subject aprovation by the professor.

Bibliography

1. Computational Intelligence: Imitating Life - Zurada, J., Marks II, R., Robinson, C. (Eds) - IEEE Press, NY, 1994.
2. Complexification - Casti, John L. - Harper Collins Publishers - NY, 1994.
3. Labyrinths of Reason - Poundstone, William - Anchor Books, New Jersey, 1988.
4. Fluid Concepts and Creative Analogies - Hofstadter, Douglas - Basic Books, NY, 1995.
5. The New Turing Omnibus - Dewdney, A. K. - Freeman & Company, NY, 1993.
6. Evolutionary Computation - Fogel, David - IEEE Press, NY, 1995.
7. Essentials of Fuzzy Modeling and Control - Yager, Ronald e Filev, Dimitar - Wiley Interscience, NY, 1994.
8. Neural Networks and Fuzzy Systems - Kosko, Bart - Prentice Hall, New Jersey, 1992.
9. Genetic Algorithms in Search, Machine Learning and Optimization - Goldberg, David - Addison Wesley Publishing Co. USA, 1989.
   1. Papers which will be indicated throughout the course.