Imperative, logical and object oriented programming. Predicate logic, CP0 and CP1. Backtracking.
The subjects main objectives are to provide students with the following knowledge:
1. To learn the general basis of artificial intelligence.
2. To apply theory to practical work. To open opportunities for a professional profile.
3. To investigate. To open opportunities for possible doctorates.
4. To work in a team.
5. To organize and plan their own work material
6. To be critical and self critical
7. To be prepared for continued learning
8. To be creative, to be able to generate new ideas
9. To acquire skills to analyze and synthesize
10. To be able to organize and plan
11. To be able to speak in public and writing correct documents in the students own language.
12. To manage information (searching and analyzing information from different sources)
13. To acquire skills and experience in problem solving
14. To acquire skills for decision making
15. To acquire skills in a second language
1 MACHINE LEARNING
1.1 Introduction
1.2 Supervised learning - classification
1.3 Unsupervised learning - association rules
1.4 Unsupervised learning - clustering
1.5 Reinforcement learning
2 PLANNING
2.1 Introduction
2.2 STRIPS
2.3 Search in the World Space
2.4 Search in the Space of Plans
2.5 Exercises
This area of study is accesible by presential and on-line students. The lecturer explains theory concepts helped by slides, whereas on-line students are helped by a study guide to follow the subject via online sessions.
During the theory sessions many practical examples are also solved.
The subject programme is imparted through theory sessions and/or the study guide, and with practical work that help students develop the capabilities for team work, field work, design implementation, results evaluation, discussion and document writing. Individual work also contributes to the assimilation of concepts.
Practical work and exercises are presented by students to the classroom audience, some of the work is discussed with respect to different possible solutions.
The practical exercises to be implemented are the following:
- Practical work 1 - Machine learning
- Individual work (recommended)
There are different methods for resolving students doubts: Individual and group assessment; an intranet forum for each subject, lecturers email, intranet´s news board; intranet subject documents for each issue, virtual classroom specially designed for on-line students.
Theory concepts represent 30% of the area of study, whereas practical exercises represent 70%. Each part is evaluated in a different way:
Theory concepts:
A. Exams
Practical exercises:
B. Public exposition exams
D. Homework
E. Individual Documents
F. Team work
G. Practical exercises using a PC
I. Presentations
The competences developed during this subject are evaluated using the following criteria:
Competence 1: To learn the general basis of artificial intelligence. [A,B,D,E,F,G,I]
Competence 2: To apply theory to practical work. To open opportunities for a professional profile. [B,D,E,F,G,I]
Competence 3: To investigate. To open opportunities for possible doctorates. [E,I]
Competence 4: To work in a team. [F,G,I]
Competence 5: To organize and plan the students own work material. [A,E]
Competence 6: To be critical and self critical. [E,F,I]
Competence 7: To be prepared for continued learning. [A]
Competence 8: To be creative, to be able to generate new ideas. [A,F,G]
Competence 9: To acquire skills for analysis and synthesis. [A,B,E,F,I]
Competence 10: To be able to organize and plan. [F,G]
Competence 11: To acquire skills for public speaking and writing correct documents in the students own language. [A,B,E,F,I]
Competence 12: To manage information (searching and analyzing information from different sources). [E,F]
Competence 13: To acquire skills and experience in problem solving. [A,F]
Competence 14:To acquire skills for decision making. [A,F]
Competence 15: To acquire skills in a second language. [B,E,F,I]
Elisabet Golobardes and Albert Orriols. Intel·ligència Artificial. Creative Commons Deed, ISBN 978-84-935665-6-2 2008
- E. Rich and K. Knight. `Artificial Intelligence´. McGraw-Hill (1994, second edition)
- M. Ginsberg. `Essentials of Artificial Intelligence´. Morgan Kaufmann Publishers, Inc. (1993)
- P.H. Winston. `Artificial Intelligence´. Addison-Wesley Publishing Company (1992, third edition)
- N.J. Nilsson. Principles of Artificial Intelligence. Morgan Kaufmann Publishers, Inc. (1980)
- Russell and P. Norvig. Artificial Intelligence: A modern approach. Prentice-Hall (1995)
- G.F. Luger and W.A. Stubblefield. Artificial Intelligence. Structures and strategies for complex problem solving. The Benjamin/Cummings Publishers Company, Inc. (1993)
- Each academic year documents (articles, links, slides,
) of each issue of the area of study is published in the intranet (E-campus).