Download INTELIGENCIA ARTIFICIAL

INTELIGENCIA ARTIFICIAL
CLAVE ****
SEPTIMO SEMESTRE
CREDITOS: 10
ASIGNATURA OBLIGATORIA
HORAS POR CLASE
Teóricas: 2.5
HORAS POR SEMANA
Teóricas: 5
HORAS POR SEMESTRE
Teóricas: 80
MODALIDAD:
CURSO-SEMINARIO
Asignatura precedente: Bases de Datos
Asignatura subsecuente: ninguna
Objetivos:
1. Que el alumno se familiarice y comprenda conceptos y los diversos algoritmos
existentes en la inteligencia artificial.
2. Que el alumno aprecie la importancia de los métodos de la inteligencia artificial.
Metodología de la enseñanza
Curso teórico. Exposición de los temas por parte del profesor, con la participación activa
de los estudiantes. Realización de ejercicios y exámenes por parte de los estudiantes.
El aspecto práctico consistirá en el entrenamiento en computadoras para la resolución
de problemas.
Evaluación del curso:
Exámenes teóricos. Participación en clase, tareas y práctica computacional.
Temario
1. Representación de conocimiento.
2. Búsquedas y heurística.
3. Reglas y árboles de decisión.
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4. Razonamiento cualitativo.
5. Lógica difusa.
6. Clustering.
7. Redes bayesianas.
8. Cadenas de Markov.
9. Modelos ocultos de Markov.
10. Algoritmos genéticos.
11. Redes Neuronales.
Bibliografía Básica
Se emplearán capítulos seleccionados de las siguientes fuentes:
1. Luger, G. F. Artificial Intelligence: Structures and Strategies for Complex
Problem Solving (4th Ed.). Addison-Wesley Longman, 2001.
2. Negnevitsky, M. Artificial Intelligence: A Guide to Intelligent Systems. AddisonWesley Longman, 2001.
3. Russell, S. J. J. y Norvig, P. Inteligencia Artificial: un enfoque moderno. Prentice
Hall, 1996.
4. Stuart C. and Shapiro Encyclopedia of Artificial Intelligence. John Wiley & Sons,
1990.
5. Winston, P. H. Artificial Intelligence. Addison-Wesley, 1992.
Bibliografía complementaria.
En el curso se emplearán adicionalmentecapítulos de libros especializados, los cuales
podrán incluir los siguientes:
1. Baader, F., Brewka, G. and Eiter, T. KI 2001: Advances in Artificial Intelligence.
Springer-Verlag, 2001.
2. Baldi, P. Brunak, S. and Brunak, S. Bioinformatics: The Machine Learning
Approach (2nd Ed.). The MIT Press, 2001.
3. Bishop, C. M. Neural Networks for Pattern Recognition. Oxford University Press,
1995.
4. Bonabeau, E., Dorigo, M. and Theraulaz, G. From Natural to Artificial Swarm
Intelligence. Oxford University Press, 1999.
5. Bratko, I. PROLOG Programming for Artificial Intelligence (3rd Ed.). AddisonWesley Longman, 2000.
6. Cawsey, A. The Essence of Artificial Intelligence. Prentice Hall, 1997.
7. Cohen, M. M. and Hudson, D. L. Neural Networks and Artificial Intelligence for
Biomedical Engineering. Wiley-IEEE Press, 1999.
8. Cohen, P. R. Empirical Methods for Artificial Intelligence. The MIT Press, 1995.
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9. Chrisley, R. L. and Begeer, S. Artificial Intelligence: Critical Concepts in
Cognitive Science. Routledge Press, 2000.
10. Chomsky N. Rules and Representations. Columbia University Press, 1982.
11. Dean, T. L., Allen, J. and Aloimonos, Y. Introductory Artificial Intelligence: Theory
and Practice. Benjamin-Cummings Publishing Co., 1994.
12. Delancey, G. Passionate Engines: What Emotions Reveal About Mind and
Artificial Intelligence. Oxford University Press, 2001.
13. Editors of Scientific American. Understanding Artificial Intelligence. Warner Books
Press, 2002.
14. Fayyad, U., Wierse, A. and Grinstein, G. G. Information Visualization in Data
Mining and Knowledge Discovery. Morgan Kaufmann Publishers, 2001.
15. Finlay, J. and Dix, A. An Introduction to Artificial Intelligence. Taylor & Francis
Press, 1996.
16. Fogel, D. B. Evolutionary Computation: Towards a New Philosophy of Machine
Intelligence. Wiley-IEEE Press, 1999.
17. Forsythe, D. E. and Hess, D. J. Studying Those Who Study Us: An
Anthropologist in The World of Artificial Intelligence. Stanford University Press,
2001.
18. Gen, M. and Cheng, R. Genetic Algorithms. John Wiley & Sons, 1999.
19. Genesereth, M. Logical Foundations of Artificial Intelligence. Morgan Kaufmann
Publishers Press, 1990.
20. Giardina, M. Neural Networks. Prentice Hall, 2002.
21. Gibas, C. G. and Jambeck, P. Developing Bioinformatics Computer Skills.
O'Reilly & Associates, 2000.
22. Ginsberg, M. L. Essentials of Artificial Intelligence. Morgan Kaufmann Publishers,
1993.
23. Gupta, M. M., Homma, N. and Jin, L. Static and Dynamic Neural Networks: From
Fundamentals to Advanced Theory. John Wiley & Sons, 2002.
24. Gurney, K. An Introduction to Neural Networks. Taylor & Francis, 1997.
25. Han, J. and Kamber, M. Data Mining: Concepts and Techniques. Morgan
Kaufmann Publishers, 2000.
26. Holland, J. H. Adaptation in Natural and Artificial Systems: An Introductory
Analysis With Applications to Biology, Control, and Artificial Intelligence. The
MIT Press, 1994.
27. Jackson, P. Introduction to Expert Systems (3rd Ed.). Addison-Wesley Longman,
1998.
28. Jefferis, D. Artificial Intelligence. Crabtree Publishing Co., 1999.
29. Kandel, A. and Backer, E. Computer-Assisted Reasoning in Cluster Analysis.
Prentice Hall, 1995.
30. Kaufman, L. and Rousseeuw, P. J. Finding Groups in Data: An Introduction to
Cluster Analysis. John Wiley & Sons, 1990.
31. Kearns, M. J. and Vazirani, U. V. An Introduction to Computational Learning
Theory. The MIT Press, 1994.
32. Kuipers, B. Qualitative Reasoning: Modeling and Simulation with Incomplete
Knowledge. The MIT Press, 1994.
33. Kurzweil, R. The Age of Spiritual Machines: When Computers Exceed Human
Intelligence. Viking Penguin Press, 1998.
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34. Langley, P. Machine Learning International Workshop. Morgan Kaufmann
Publishers, 2002.
35. Leondes, C. T. Expert Systems. Academic Press, 2001.
36. Mackay, D. Information Theory, Inference and Learning Algorithms. Cambridge
University Press, 2001.
37. Minker, J. Logic-Based Artificial Intelligence. Kluwer Academic Publishers, 2000.
38. Mitchell, M. An Introduction to Genetic Algorithms. The MIT Press, 1998.
39. Mitchell, T. M. Machine Learning. McGraw-Hill, 1997.
40. Miyamoto, S. Fuzzy Sets in Information Retrieval and Cluster Analysis. Kluwer
Academic Publishers, 1990.
41. Neapolitan, R. E. Probabilistic Reasoning in Expert Systems: Theory and
Algorithms. John Wiley & Sons, 1990.
42. Nilsson, N. J. Artificial Intelligence: A New Synthesis. Morgan Kaufmann
Publishers, 1998.
43. Nilsson, N. J. Principles of Artificial Intelligence. Morgan Kaufmann Publishers,
1994.
44. Norvig, P. Paradigms of Artificial Intelligence Programming: Case Studies in
Common LISP. Morgan Kaufmann Publishers, 1992.
45. Pearl, J. Heuristics: Intelligent Search Strategies for Computer Problem
Solving. Addison-Wesley, 1984.
46. Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible
Inference. Morgan Kaufmann Publishers, 1988.
47. Periaux, J., Neittaanmaki, P. and Miettinen, K. Evolutionary Algorithms in
Engineering and Computer Science: Recent Advances in Genetic Algorithms,
Evolution Strategies, Evolutionary Programming. John Wiley & Sons, 1999.
48. Perry, R. L. Artificial Intelligence. Watt Franklin Press, 2000.
49. Pfeifer, R. and Scheier, C. Understanding Intelligence. The MIT Press, 1999.
50. Pratt, I. Artificial Intelligence. Scholium International Press, 1994.
51. Ralston, A., Reilly, E. D. and Hemmendinger, D. Encyclopedia of Computer
Science (4th Ed.). Nature Publishing Group, 2000.
52. Sakawa, M. Genetic Algorithms and Fuzzy Multiobjective Optimization. Kluwer
Academic Publishers, 2001.
53. Shafer, G. A Mathematical Theory of Evidence. Princeton University Press, 1976.
54. Steven, L. L. and Tanimoto, W. H. The Elements of Artificial Intelligence Using
Common LISP. (2nd Ed.). W. H. Freeman & Co., 1993.
55. Terano. T. and Liu, H. Knowledge Discovery and Data Mining: Current Issues
and New Applications. Springer-Verlag, 2001.
56. Tracy, K. W. and Bouthoorn, P. Object-Oriented Artificial Intelligence Using C++.
W. H. Freeman & Co., 1997.
57. Williams, S. Touching the Grail: The Resurgent Debate Over Artificial
Intelligence. Random House Press, 2002
58. Winograd T. Representation and Understanding. Academic Press, 1976.
59. Wooldridge, M. J. and Veloso, M. M. Artificial Intelligence Today: Recent Trends
and Developments. Springer-Verlag, 1999.
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Perfil profesiográfico.
Dada la actualidad y profundidad que se desea en cada una de las asignaturas del
programa, se emplearán preferentemente investigadores con doctorado, que laboren en
temas relacionados a la asignatura. En casos particulares, el Comité Académico podrá
autorizar la participación de estudiantes doctorales avanzados o de profesores con
experiencia en la temática de la asignatura.
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