ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 30 Interactive Classroom: 20 Total: 51
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: Second Semester
Teaching: With teaching
Enrolment: Enrollable
Know and know how to apply the main strategies of artificial intelligence for solving search problems in different scenarios (informed, uninformed and adversarial search), constraint satisfaction and planning.
Once the student has passed the course:
- Know how to apply and implement search methods with informed and uninformed strategies in problems represented in state spaces.
- Be able to solve adversarial search problems
- You will be able to solve search and optimization problems with constraints.
- You will learn different problem solving algorithms based on the search in a space of possible configurations.
- Know and know how to model and solve basic planning or scheduling problems.
T1: Historical perspective of AI
T2: Problem solving by means of state-space search
T3: Uninformed and informed search strategies
T4: Constraint satisfaction problems
T5: Adversarial search and games
T6: Automatic planning
Basic bibliography:
1. J. Palma, R. Marín (eds.). Artificial Intelligence. Methods, techniques and applications. McGrawHill (2008). ISBN: 9788448156183
Fernández Galán, S., González Boticario, J., Mira Mira, J. Solved Problems of Applied Artificial Intelligence. Search and Representation. Addison Wesley (1998). ISBN: 9788478290178
3. Russell, S., Norvig, P. Artificial Intelligence (A Modern Approach), (4th Edition Global Edition, 2022). ISBN: 9781292401133. (2nd edition also in Spanish).
4. Handbook of Artificial Intelligence. Springer-Verlag, 2015. ISBN 978-3-662-43505-2.
Supplementary Bibliography
5. 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.
7. Artificial intelligence for developers. Virginie Mathivet. ENI Editions, 2015.
8. Artificial Intelligence Course. Fernando Sancho Caparrini. http://www.cs.us.es/~fsancho
Contribute to achieve the competences included in the memory of the degree in Artificial Intelligence, especially:
BASIC AND GENERAL
CG3 - Ability to design and create quality AI-based models and solutions that are efficient, robust, transparent and accountable.
CG4 - Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and propose new methods based on artificial intelligence.
CG5 - Ability to conceive new computational systems and/or evaluate the performance of existing systems, integrating artificial intelligence models and techniques.
CB2 - That students know how to apply their knowledge to their work or vocation in a professional manner and possess the competencies that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of study.
CB4 - Students are able to convey information, ideas, problems and solutions to both specialized and non-specialized audiences.
CROSSCUTTING
TR1 - Ability to communicate and transmit knowledge, skills and abilities.
TR3 - Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
TR5 - Ability to develop models, techniques and solutions based on artificial intelligence that are ethical, non-discriminatory and reliable.
SPECIFIC
CE12 - Know the basics of artificial intelligence algorithms and models for solving problems of certain complexity, understand their computational complexity and have the ability to design new models.
The didactic methodology will be based on individual work -although sometimes in groups-, discussion with the teacher in class and individual tutorials.
For each topic or thematic block of the expository classes, the teacher will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliography, provide them with additional work material, etc. In the expository classes the competences CG3, CG4, CG5, CE12 will be worked on. In addition, the professors will propose to the students a set of activities to be carried out, individually or in groups (works, presentations, readings, practices, etc.). In general, the students will have to present them to the teacher for evaluation, for which the deadlines for delivery/presentation will be indicated through the channels used for student-teacher communication. The practices and part of the interactive sessions will be developed in the Computer Classroom of the School, using different software tools for each of the thematic blocks. These activities will allow the development of the competences CG5, CB2, CB4, TR1-3, CE12.
Students will work individually or in small groups, with the constant support of the faculty. There will be scripts of practices, seminars and other activities to be carried out individually or in small groups.
Teaching will be supported by the USC virtual platform in the following way: repository of documentation related to the subject (texts, presentations, recommended readings...) and virtual tutoring of students (e-mail, forums).
The learning evaluation considers both the theoretical part (40%), the practical part (50%) and other activities (10%). In order to pass the subject an overall grade of 5 or higher must be obtained, out of a maximum score of 10 points, according to the following criteria:
- Theoretical part: this will be assessed in a single exam to be taken on the official date. The grade of the exam must be equal to or higher than 4 out of a maximum of 10 points in order to pass the whole subject. Otherwise, it will have to be repeated at the make-up exam.
- Practical part: evaluation of all the practical activities proposed by the teachers in the interactive sessions. The grade for this part will be the average of the grades for the practical activities, with the weighting established in the presentation of the subject, provided that a grade equal to or higher than 3 is obtained in all of them. In this case, the overall grade 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. Those practicals with a grade lower than 3 points must be assessed at the second opportunity.
- Other Activities: evaluation of other proposed compulsory activities (delivery of work or exercises, presentations and participation in the classroom, attendance at lectures and participation in technical visits, etc.). The overall grade for this part will be the average of the grades for the proposed activities, with the weighting specified in the presentation of the subject, and provided that all of them have a grade equal to or higher than 3. In this case, the overall grade 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. Those activities with a grade lower than 3 points must be assessed at the second opportunity.
The final grade of the subject will be the arithmetic average weighted by the percentages indicated above of the theoretical, practical and complementary activities 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.
Students who have not taken the exam or submitted to the evaluation of any other compulsory activity will receive a grade of "no-show".
In order to pass the course in the second opportunity, the student must undergo the evaluation of all those compulsory parts pending, according to 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 of the regulations for the 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 ETSE regulations 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 grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).
Classroom work time: 51 total hours, divided into 20h (lectures), 30h (seminars and practicals), 1h (tutorials).
Personal work time: 99h (total), divided into 39h (autonomous study of theory and practices) and 60h (work, projects and other activities).
It is recommended that the 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 tutorials for the resolution of doubts.
It is recommended to have passed the subjects "Mathematical Optimization", "Programming I", "Programming II" and "Algorithms".
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
| Thursday | |||
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| 16:30-17:30 | Grupo /CLE_01 | Spanish | IA.11 |
| 17:30-20:00 | Grupo /CLIL_01 | Spanish | IA.11 |
| Friday | |||
| 15:30-18:00 | Grupo /CLIL_02 | - | IA.14 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_03 | IA.01 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_01 | IA.01 |
| 05.28.2025 09:00-14:00 | Grupo /CLE_01 | IA.01 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_02 | IA.01 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_03 | IA.11 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_01 | IA.11 |
| 05.28.2025 09:00-14:00 | Grupo /CLE_01 | IA.11 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_02 | IA.11 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_03 | IA.12 |
| 05.28.2025 09:00-14:00 | Grupo /CLE_01 | IA.12 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_01 | IA.12 |
| 05.28.2025 09:00-14:00 | Grupo /CLIL_02 | IA.12 |
| 07.04.2025 09:00-14:00 | Grupo /CLIL_02 | IA.11 |
| 07.04.2025 09:00-14:00 | Grupo /CLIL_03 | IA.11 |
| 07.04.2025 09:00-14:00 | Grupo /CLE_01 | IA.11 |
| 07.04.2025 09:00-14:00 | Grupo /CLIL_01 | IA.11 |