The subject "Artificial Intelligence" is a compulsory subject for the students of the specialty of Computing within the Degree in Computing and optional for the students of the other specialties. It is situated in the third year of the degree and is complemented by two other subjects in the specialty: Data Mining and Algorithms Design. In this subject are developed:



- The basic concepts of Artificial Intelligence. That is, the ability to reproduce human reasoning and language skills in a computer.

- Resources that allow searching for relevant information in large volumes of information with the help of heuristics. For example, what is the most interesting movement to make at a given moment in a chess game.

- The methods of representing knowledge from experts or bibliography in a way that is executable by a computer and facilitates collaborative work with the expert. Ontologies are common resources in the representation of knowledge.

- The use of specific tools for automatic reasoning about the represented knowledge to offer the appropriate recommendations for a particular case. For example, the diagnosis of a disease.

- Knowledge Engineering capabilities. Process-led development, knowledge reuse and automation of prototype generation.

- Construction of systems based on fuzzy logic to incorporate imprecise knowledge.

- Basic concepts of Automatic Learning for obtaining knowledge from data.





The student will be able to study, in the fourth year of the Degree, elective subjects related to the construction of intelligent systems: Automatic Learning and Neural Networks, Knowledge-based

Systems, Advanced Techniques of Artificial Intelligence, Logic Programming, Natural Language Processing, Robotics and Intelligent Control, Heuristic Search and Computer Vision.



The basic requirement to take this course is the previous knowledge of an object oriented programming language.



The contents of the subject open the way to the integration of knowledge and reasoning in all types of computer systems. For example, recommender system, medical diagnosis, risks forecasting, logistics, etc. Artificial Intelligence is a well established area from the scientific point of view and is in full expansion both in the area of ​​R+D+I and in its industrial application.

The main objective of Artificial Intelligence is the incorporation of intelligent behavior into computer systems. That is, it is about providing these systems of the capacities of perception, reasoning and action.



GENERAL COMPETENCES

In addition to the competences of the subject and depending on the contents of the course, the general skills C1, C2, C3, C4, C5 and C12 and the ones that are specific of the specialty of Computing CC1, CC3, CC4, CC5 and CC7 mentioned in this document.



TRANSVERSAL COMPETENCES.

The general competencies CB4, C8 and C9 mentioned in this document.



SPECIFIC COMPETENCES

With this course, students will be able to:



- Get an overview of the areas of application of Artificial Intelligence.

- Identify the types of problems dealt with by Artificial Intelligence and know some generic methods to solve them.

- Be aware of the importance of knowledge in solving problems as well as the techniques that can be used for their representation and execution.

- Work over the aspects of uncertainty handle in the representation of knowledge, acquiring sufficient skills in the theoretical and practical aspects of Fuzzy Logic.

- Identify the basic problems that arise when building systems based on knowledge and the engineering solutions that can solve them.

- Discover the possibility of incorporating learning into intelligent systems.

1. Introduction

1.1. Definition, evolution, importance and objectives of Artificial Intelligence.

1.2. Areas of application of Artificial Intelligence.

1.3. Tools, languages and development environments for Artificial Intelligence.

1.4. Construction of knowledge-based systems.



2. Problem solving through search.

2.1. Characterization of problems through a State Space.

2.2. Heuristic search techniques on a State Space.

2.3. Useful algorithms for games.



3. Classic models of knowledge representation and reasoning.

3.1. Known models of knowledge representation: predicate logic, probabilistic models and fuzzy logic.

3.2. Systems based on production rules. EHSIS development environment (CLIPS and FuzzyCLIPS).

3.3. Representation of data through facts and objects.

3.4. Knowledge representation: Rules and functions.

3.5. Reasoning: Inference directed by data and by objectives.

3.6. Concepts and relationships. Ontologies

3.7. Methodologies and development tools: systematization and automation.



4. Representation of uncertain knowledge.

4.1. Introduction.

4.2. Fuzzy Logic based knowledge systems.

4.3. Production systems based on fuzzy logic.



5. Machine learning

5.1. Supervised and unsupervised learning.

5.2. Neural networks feedforward with backprogation.

5.3. Application development: modeling, training and testing.

Lecturer sessions of concept presentation (reinforced with examples) will be interspersed with the exercises (individual, group, etc.).



In the laboratories, a set of exercises distributed in advance will be implemented. The sessions require a previous preparation work on these exercises. During the laboratory sessions the teacher will guide the students and resolve the doubts that arise in solving the problems raised.

• Continuous Assessment System
• Final Assessment System
• Tools and qualification percentages:
  • Written test to be taken (%): 50
  • Realization of Practical Work (exercises, cases or problems) (%): 35
  • Team projects (problem solving, project design)) (%): 15

There are two modalities of evaluation of the subject: global exam (at the end), or continuous assessment.



Continuous assessment is optional, and requires active participation by the student. Therefore, the student must attend both to class as to the laboratories performing the proposed activities (exercises, work, practices, explanations ...).



Students must submit in writing to the faculty responsible for the subject the resignation to the continuous evaluation, for which they will have a period of 9 weeks from the beginning of the semester.



In the previous table, in summary form, the evaluation tools and the weight of each one are indicated.



The subject has a weight of 6 ECTS credits, equivalent to 60 hours, with 2 practical credits and 4 theoretical ones. Therefore, the weight in the final grade of the practical part is 1/3 and that of the theoretical 2/3.



The evaluation is continuous in 18 weeks:

- 60% of the points are obtained during the class period

- 40% remaining in the ordinary call



Each evaluation will have laboratory work that will be carried out in groups and will be evaluated individually through a quiz: the individual failure to pass the test implies to get in the laboratory a failing grade.

Students who do not pass the subject in the ordinary call will be submitted to the extraordinary global exam with all the contents of the subject. The points of the practical contents will be obtained with the realization of practical exercises. The grades of the practical laboratories carried out during the course will not be maintained.

All the software used for the subject is free. Clips programming language: http://www.clipsrules.net/ Protégé 3.5 ontology editor (Frames Protégé) https://protegewiki.stanford.edu/wiki/Protege_Desktop_Old_Versions

Basic bibliography

- Russell, S.; Norvig, P. (2009): Artificial Intelligence: A Modern Approach (third edition). Prentice Hall. ISBN 0-13-604259-7

- Russell, S.; Norvig, P. (2003): Inteligencia Artificial ¿ Un Enfoque Moderno (2ª ed.). Prentice Hall Hispanoamericana

- Nilsson, N. (2001): Inteligencia Artificial ¿ Una Nueva Síntesis. McGraw-Hill

- Rich, E.; Knight, K. (1994): Inteligencia Artificial (2ª ed.). McGraw-Hill

- Giarratano, J., Riley, G. "Expert Systems: Principles and Programming". PWS Publishing Company. 1994.

- Giarritano, J., Riley, G., PWS PC Sistemas Expertos. Principios y Programación, Thomson 2001.

In-depth bibliography

- Luger, G. F., Stubblefiel, W.A. "Artificial Intelligence and the Desing of Expert Systems". The Benjamin/cummings Publishing Company. Ed. 1993. ¿ - Norvig, P. ¿Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp¿, Morgan Kaufmann Publishers, 1992. - Steel, G. "Common Lisp: The language". Ed. Digital Press, 1984. - Winston, H.P.. "Lisp". Ed. Addison-Wesley, 1989. - Giarritano, J., Riley, G., PWS PC "Sistemas Expertos. Principios y Programación", Thomson 2001. - CLIPS Version 6.22. User's Guide & Reference Manual. NASA L.B. Johnson Space Center, 2004

Journals

- 'Inteligencia Artificial', Revista Iberoamericana de IA. - AI in the News (Servicio de AAAI) - Journal of Artificial Intelligence Reseach (JAIR). - Cognitive Systems Research (Elsevier). - Artificial Intelligence

Web addresses

http://www.aepia.org/ http://www.aaai.org http://www.emagister.com/tutorial/tutoriales-inteligencia-artificial-kwes-2020.htm http://www.abcdatos.com/tutoriales/programacion/inteligenciaartificial.html..