ECTS credits ECTS credits: 4.5
ECTS Hours Rules/Memories Student's work ECTS: 71.5 Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 30 Total: 112.5
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable
Agents applying problem solving methods use state representations and solutions to obtain a solution to a problem that is not always optimal, but is of sufficient quality for the time and computational resources available. Students will know and know how to apply the most common general-purpose algorithms and heuristics for solving problems with state representations, adversarial search and constraint satisfaction.
1. Introduction to intelligent agents
2. Search strategies
- Optimization and Search
- Local search and heuristic search
- Search with restrictions
3. Trajectory-based metaheuristics
- Introduction
- Simulated cooling
4. Population-based Search Metaheuristics
- Bio-inspired computing
- Genetic algorithms
- Ant Colony Algorithms
- Particle Swarm Algorithms
- Genetic Programming
5. Introduction to multi-objective optimization
6. Search among adversaries
- Two-agent games
- Minimax and Alpha-Beta Algorithms
- Evaluation functions
- Stochastic Games
BASIC BIBLIOGRAPHY
Inteligencia Artificial. Técnicas, métodos y aplicaciones. McGraw-Hill, 2008. ISBN 978-84-481-5618-3.
Handbook of Artificial Intelligence. Springer-Verlag, 2015. ISBN 978-3-662-43505-2.
Russell, S., Norvig, P. Inteligencia Artificial (Un Enfoque Moderno), Segunda ed. Prentice-Hall International. (2004). ISBN: 9789688806821 (4ª ed. En inglés, 2020).
Nilsson, N.J. Inteligencia artificial (Una nueva síntesis). McGraw-Hill. (2001). ISBN: 9788448128241
COMPLEMENTARY BIBLIOGRAPHY
Rossi, Van Beek, Walsh (2006) Handbook of Constraint Programming, Elsevier.
Joseph Y-T. Leung (2004) Handbook of Scheduling: Algorithms, Models, and Performance Analysis, Chapman and Hall/CRC.
Inteligencia artificial para desarrolladores. Virginie Mathivet. ENI Ediciones, 2015.
Curso de Inteligencia Artificial. Fernando Sancho Caparrini. http://www.cs.us.es/~fsancho
The main expected learning outcomes are:
- To know the formulation of certain sets of problems for which a solution is represented as a sequence of actions that allows to reach a certain objective.
- Learn to design a computable representation for goal-based problems from a set of states (initial, goal and search space).
- To know and learn how to apply the most representative techniques of uninformed search in a state space (in depth, in width and its variants), and to know how to analyze their efficiency in time and computational space.
- Know and learn how to apply the most representative state space informed search techniques (A * and local search), particularly in optimization problems.
- Understand the notion of heuristics and analyze the time and space efficiency implications of search algorithms.
- Know and learn to apply the basic techniques of searching with an opponent (minimax, alpha-beta pruning) and their relation to the games.
- Recognize the possibility of representing the internal structure of states from a formulation based on a set of variables that must be assigned to find a solution that satisfies a set of constraints.
- Analyse the characteristics of a given problem and determine whether it can be tackled using search techniques. Select the most appropriate technique to solve it and apply it.
- Program any of these techniques in a general purpose programming language.
In addition, it contributes to the development of the general and specific competences included in the memory of the Degree in Computer Engineering of the USC:
BASIC AND GENERAL
CG8 - Knowledge of basic subjects and technologies, which enable them to learn and develop new methods and technologies, as well as those that provide them with great versatility to adapt to new situations.
CG9 - Ability to solve problems with initiative, decision-making, autonomy and creativity. Ability to know how to communicate and transmit the knowledge, skills and abilities of the profession of Technical Engineer in Computer Science.
CROSS-CUTTING
TR1 - Instrumental: Capacity for analysis and synthesis. Capacity for organization and planning. Oral and written communication in Galician, Spanish and English. Capacity for information management. Problem solving. Decision-making.
TR2 - Personal: Teamwork. Working in a multidisciplinary and multilingual team. Skills in interpersonal relationships. Critical reasoning. Ethical commitment.
TR3 - Systemic: Autonomous learning. Adaptation to new situations. Creativity. Initiative and entrepreneurial spirit. Motivation for quality. Sensitivity towards environmental issues.
SPECIFIC
RI15 - Knowledge and application of the fundamental principles and basic techniques of intelligent systems and their practical application
The teaching methodology will be based essentially on individual work, although it will sometimes be carried out in groups, mainly in discussion with the teaching staff in expository and interactive classes.
For each topic or thematic block of the lectures, the teaching staff will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliographic resources and will provide additional work material, mainly exercises related to the theoretical concepts. In the lectures, students will work on the competences CG8, CG9, TR1, TR3, RI15. In addition, the lecturers will propose a set of activities to be carried out individually or in groups (cases, exercises) that students must hand in for assessment, in accordance with the deadlines set. These activities will allow the development of the competences CG8, CG9, TR1-3, RI15.
The practicals and part of the interactive sessions will take place in the school's IT classroom, using various software tools and developing applications for each of the thematic blocks. The practical sessions will develop the competences CG8, CG9, TR1-3, RI15.
Students will work individually or in small groups, with constant monitoring and tutoring by the teaching staff. Practice scripts will be provided with the tasks to be carried out individually or in small groups.
The teaching will be supported by the USC virtual platform in the following way: repository of the documentation related to the subject (texts, presentations, exercises, practice scripts, ...) and virtual tutoring of students (e-mail, forums).
The learning assessment considers both the theoretical part (40%) and the practical part (60%). In order to pass the subject, an overall mark of 5 or more out of a maximum of 10 points must be obtained, in accordance with the following criteria:
- Theoretical part (40%): this will be assessed in a single exam to be taken on the official date and by means of exercises. The grade for both parts must be equal to or higher than 4 out of a maximum of 10 points in order to pass the subject as a whole. Otherwise, it will have to be repeated at the make-up session. The grade for this part will be obtained as the arithmetic mean of the two evaluation items (exam 60% and exercises 40%).
- Practical part (60%): evaluation of all the interactive activities of compulsory delivery proposed, foreseen at the end of sessions 2, 4, 7, 9 and 12.
The grade for this part must be equal to or higher than 4 out of a maximum of 10 points in order to pass the whole subject. In any case, those deliveries with a grade lower than 3 points must be re-evaluated at the second opportunity. The evaluation of the practical part will consider the delivery, the results obtained and the presentation and discussion of the same with the teaching staff.
The assessment of interactive practices does not end with the delivery of the practice, but may include the completion of a self-assessment questionnaire and/or a face-to-face presentation and discussion session.
These assessment activities will be compulsory and may be carried out in interactive class, so that, for the purposes of the provisions of Art.1 of the Regulations on class attendance in official undergraduate and master's degree courses at the University of Santiago de Compostela (25/11/2024) ", attendance at the sessions where these activities are scheduled will be compulsory, being a requirement that, if not fulfilled, will result in a grade of 0.0 in the corresponding deliverable.
Except as indicated in this section, class attendance will not be assessed in the evaluation system, although attendance at the different teaching activities helps to improve understanding of the subject and the acquisition of competences.
All deliveries will have the same weight in the practical grade.
The mark for this part must be equal to or higher than 4 out of a maximum of 10 points in order to pass the subject as a whole.
Those internships with a grade lower than 3 points must be assessed at the second opportunity.
The final grade for the subject will be the arithmetic average weighted by the percentages indicated above for the theoretical and practical parts and complementary activities, unless the established minimum thresholds are not reached in any assessment item.
If one or more parts do not achieve the minimum mark required to pass the subject overall, the final mark for the opportunity will be the minimum of the marks obtained in those parts.
Students who have not taken the exam or who have taken the assessment of any other compulsory activity will receive a mark for not having taken the exam.
In order to pass the subject at the second opportunity, students must undergo the assessment of all the compulsory parts pending, as specified above.
For the rest, the grades obtained during the course will be retained.
Repeating students must follow the same evaluation system as the rest of the students.
In the case of fraudulent performance of exercises or tests, the provisions of the regulations for assessing students' academic performance and reviewing grades will apply (https://www.xunta.gal/dog/Publicados/2011/20110721 /AnnouncementG2018-190711-4180_gl.html).
In application of the ETSE regulations on plagiarism (approved by the ETSE Board on 19/12/2019), the total or partial copying of any practice or theory exercise will result in the failure of both opportunities of the course, with a grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).
Classroom work time: 41 total hours, divided into 10h (theoretical teaching), 30h (interactive practical teaching), 1h (tutorials).
Personal working time: 71,5h (total).
It is recommended that students solve, implement, verify and validate all the proposed exercises and practices (not only the evaluable ones). It is also considered important to make an intense use of the tutorials for the resolution of doubts and an active participation in the expository and interactive sessions.
It is recommended to have passed the subject "Artificial Intelligence".
The course will be taught in Spanish and Galician, but the bibliography, references and notes may contain content in English.
Alberto Jose Bugarin Diz
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816440
- alberto.bugarin.diz [at] usc.es
- Category
- Professor: University Professor
Alejandro Catala Bolos
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- alejandro.catala [at] usc.es
- Category
- PROFESOR/A PERMANENTE LABORAL
| Monday | |||
|---|---|---|---|
| 11:30-14:00 | Grupo /CLIL_01 | Spanish | IA.S2 |
| Tuesday | |||
| 09:00-11:30 | Grupo /CLIL_02 | Spanish | IA.S2 |
| 18:00-19:00 | Grupo /CLE_01 | Spanish | IA.S1 |
| 01.12.2026 16:00-20:00 | Grupo /CLIL_02 | Classroom A2 |
| 01.12.2026 16:00-20:00 | Grupo /CLE_01 | Classroom A2 |
| 01.12.2026 16:00-20:00 | Grupo /CLIL_01 | Classroom A2 |
| 07.06.2026 10:00-14:00 | Grupo /CLIL_01 | IA.11 |
| 07.06.2026 10:00-14:00 | Grupo /CLIL_02 | IA.11 |
| 07.06.2026 10:00-14:00 | Grupo /CLE_01 | IA.11 |