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 didactic methodology will be based essentially on individual work, although sometimes it will be developed in groups, mainly in discussion with the teaching staff in expository and interactive classes.
For each topic or thematic block of the lectures, the teacher will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliographic resources and provide additional work material, mainly exercises related to the theoretical concepts. In the expository classes the competences CG8, CG9, TR1, TR3, RI15 will be worked on. In addition, the teacher will propose to students a set of activities to be carried out individually or in groups (cases, exercises) that students must submit for evaluation, according to the deadlines. These activities will allow the development of the competences CG8, CG9, TR1-3, RI15.
The practices and part of the interactive sessions will be developed in the School's Computer Room, using various software tools and developing applications for each of the thematic blocks. The realization of the practices will allow to develop the competences CG8, CG9, TR1-3, RI15.
Students will work individually or in small groups, with constant monitoring and tutoring from the teacher. 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 as follows: repository of 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 equal to or higher than 5, out of a maximum of 10 points, must be obtained, according to the following criteria:
- Theoretical part: will be evaluated in a single exam to be held on the official date and by performing exercises. The grade for both parts must be equal to or higher than 4 out of a maximum score of 10 points in order to pass the subject as a whole. Otherwise, it will have to be repeated in the recovery opportunity. The grade of this part will be obtained as the arithmetic mean of the two evaluation items (exam 60% and exercises 40%).
- Practical part: evaluation of all the interactive activities of compulsory delivery proposed by the teacher. The grade for this part must be equal to or higher than 4 out of a maximum score of 10 points in order to pass the subject as a whole. 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 final grade of the subject will be the arithmetic average weighted by the percentages indicated above of the theoretical and practical parts. In case of incurring in any of the situations indicated above for not reaching in one or more parts the minimum grade required to pass the subject globally, the final grade of the opportunity will be the minimum of the grades obtained in those parts. The parts that do not reach the minimum grade will have to be repeated in the second opportunity.
Students who have not taken the exam and have not submitted to the evaluation of any other compulsory activity will obtain the grade of not presented.
In order to pass the course in the second opportunity, students must undergo the evaluation of all those compulsory parts pending, in accordance with the above specified. For the rest, the grades obtained during the course will be retained.
In the case of fraudulent performance of exercises or tests, it will apply the provisions contained in the rules of evaluation of the academic performance of students and review of grades (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…). In application of the regulations of the ETSE on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copy of any exercise of practices or theory will mean the failure of the two opportunities of the course, with the qualification 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 course "Algorithms and Data Structures".
The course will be taught in Spanish and Galician, but part of the contents, bibliography or other references may be 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
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Nikolay Babakov
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- nikolay.babakov [at] usc.es
- Category
- Predoutoral Marie Curie
| Monday | |||
|---|---|---|---|
| 09:00-11:30 | 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 |
| 01.10.2025 10:00-14:00 | Grupo /CLIL_02 | Classroom A1 |
| 01.10.2025 10:00-14:00 | Grupo /CLIL_01 | Classroom A1 |
| 01.10.2025 10:00-14:00 | Grupo /CLE_01 | Classroom A1 |
| 07.07.2025 16:00-20:00 | Grupo /CLIL_02 | Classroom A3 |
| 07.07.2025 16:00-20:00 | Grupo /CLIL_01 | Classroom A3 |
| 07.07.2025 16:00-20:00 | Grupo /CLE_01 | Classroom A3 |