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.
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. Addison-Wesley 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.). TheMIT 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.). Addison-Wesley 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.
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.
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.
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.
