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
The objective of the subject is to provide the necessary skills to build systems capable of solving problems using knowledge and reasoning similar to how a human would do. The subject will focus on knowing how to define the knowledge that a system requires to give it intelligent behavior, on modeling and representing that knowledge in a symbolic way, and on automatically reasoning about those representations, with the ultimate goal of enabling the system to perform intelligent actions. For this purpose, knowledge representations such as those supported by descriptive logics, ontologies, or semantic graphs will be used.
Topic 1: Introduction to Knowledge Representation
Topic 2: Knowledge Representation in Rule-Based Systems
Topic 3: Inference Methods in Rule-Based Systems
Topic 4: Ontologies
Topic 5: Descriptive Logic
Topic 6: Knowledge Graphs
BIBLIOGRAFÍA BÁSICA
[1] A. Gomez-Pérez, M. Fernández, O. Corcho (2003): Ontological Engineering. Springer. [SIG.: C60 456, Escuela de Ingeniería]
[2] J.T. Palma Méndez, R. Marín Morales (2008): Inteligencia artificial: métodos, técnicas y aplicaciones. McGraw Hill [Sig.: A360 15, Escuela de Ingeniería]
BASIC SKILLS
[CB2] Students should be able to apply their knowledge to their work or vocation in a professional manner and possess the competencies typically demonstrated through argumentation, problem-solving, and defense within their field of study.
[CB4] Students should be able to convey information, ideas, problems, and solutions to both specialized and non-specialized audiences.
[CB5] Students should have developed the learning skills necessary to undertake further studies with a high degree of autonomy.
GENERAL SKILLS
[CG2] Ability to solve problems with initiative, decision-making, autonomy, and creativity.
[CG3] Ability to design and create quality solutions and models based on Artificial Intelligence that are efficient, robust, transparent, and responsible.
[CG4] Ability to select and justify the appropriate methods and techniques to solve specific problems or to develop and propose new methods based on artificial intelligence.
[CG5] Ability to conceive new computer systems and/or evaluate the performance of existing systems that integrate models and techniques of artificial intelligence.
SPECIFIC SKILLS
[CE13] Ability to model and design systems based on representation of knowledge and logical or approximate reasoning and apply them to different domains and problems, also in contexts of uncertainty.
[CE14] Know semantic technologies for the storage and access of knowledge graphs and their use in solving problems.
TRANSVERSAL SKILLS
[TR3] Ability to autonomously and creatively create new models and solutions, adapting to new situations. Initiative and entrepreneurial spirit.
The teaching methodology is aimed at focusing on the practical and theoretical aspects of knowledge representation and symbolic reasoning, with an emphasis on the characteristics and differences compared to other paradigms of Artificial Intelligence. Therefore, students should be capable of understanding the advantages of this approach and developing programs that make use of knowledge, using ontologies and production rules. With this in mind, three types of learning activities are distinguished: lectures and small group sessions. They are as follows:
(1) Lectures (20 hours) are designed to explain the different approaches to knowledge representation in Artificial Intelligence, as one of the fundamental paradigms on which it is based. In general, these lectures will be divided into two main blocks: knowledge-based systems and ontologies/descriptive logic. Although these blocks are complementary and address different aspects of knowledge representation and reasoning.
(2) Practical sessions in small groups (30 hours) aim to provide students with the skills to implement programs and solve problems that require knowledge representation and reasoning to extract new information. Therefore, it is important that these practical sessions include a sufficiently extensive set of exercises that utilize different techniques of knowledge representation and reasoning. Attendance to these classes is MANDATORY.
Taking into account this teaching methodology, competencies CE19, CE20, CE21, and CE22 have specific contents that are linked to the theoretical and practical aspects of a subject like Knowledge Representation and Reasoning, in which the paradigms of knowledge representation and symbolic reasoning are reviewed. These competencies are explicitly assessed in the exams carried out throughout the course, just like competencies CG2, CG3, CG4, and CG5, which, although they are general competencies, are necessary to adequately develop the proposed exercises.
On the other hand, competency TR3 will be developed through practical exercises, as they require the analysis of the presented problems and the synthesis of knowledge representation and reasoning concepts in their resolution. Competencies CB2, CB4, and CB5 will also be addressed in the theoretical classes, where student participation will be encouraged.
The assessment of the course will take place in two different yet complementary ways, aiming to evaluate the competence in the practical development of knowledge-based systems and ontologies. Additionally, a distinction will be made between regular assessment and recovery assessment:
REGULAR OPPORTUNITY
(1) Exam to demonstrate mastery of the theoretical aspects of knowledge representation and symbolic reasoning. This exam will consist of a set of questions covering the theory topics of the course. This part will account for 70% of the final grade.
(2) Completion of a set of exercises that practically demonstrate proficiency in developing knowledge-based systems, ontologies, knowledge graphs, and reasoning methods. Each exercise will be evaluated individually. This part will constitute 30% of the final grade.
In order to overcome the matter, it is necessary to approve the two parties separately.
Finally, if any of the proposed exercises are submitted, it will be considered as fulfilling the requirements of the course. However, partial or complete copying of one or more exercises will result in a failing grade for the entire course.
RECOVERY OPPORTUNITY
The evaluation criteria for the theory and practical parts in the recovery opportunity will be exactly the same as in the regular opportunity. Therefore, in addition to passing the theory exam and exercises, attendance to interactive practical sessions (following the attendance criteria specified below) is necessary to pass the course.
ATTENDANCE CONTROL
As mentioned earlier, attendance to the interactive practical sessions is mandatory as key concepts of the course are addressed in those sessions. Attendance will be monitored through sign-in sheets that need to be completed at the end of each session. Additionally, if attendance to less than 80% of the interactive practical sessions is recorded, the course will be considered as not passed.
In cases of fraudulent completion of exercises or exams, the regulations outlined in the "Normativa de evaluación del rendimiento académico de los estudiantes y de revisión de calificaciones" (Regulation for the evaluation of academic performance and review of grades) will be applied. Furthermore, in accordance with the plagiarism regulations of the ETSE (approved by the Xunta da ETSE on 19/12/2019), any total or partial copying of practical or theory exercises will result in a failing grade in both opportunities of the course, with a grade of 0.0 in both cases.
Classroom work:
- Theoretical classes: 20 hours
- Practical classes: 30 hours
- Individual tutoring: 1 hour
Total classroom work hours: 51 hours
Students' personal work (study, exercises, practices, projects) and other activities (assessment): 99 hours.
Total work hours: 150 hours.
Due to the strong interrelation between the theoretical and practical aspects, as well as the progressive presentation of closely related concepts in the theory section, it is recommended to dedicate daily study or review time.
The USC virtual campus will be used for all teaching activities, including material publication, practice guidelines, and assignment submissions.
The preferred language for delivering lectures and interactive classes is Galician.
Manuel Lama Penin
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816427
- manuel.lama [at] usc.es
- Category
- Professor: University Professor
David Chaves Fraga
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815525
- david.chaves [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Tomás Benavides Álvarez
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- tomas.benavides.alvarez [at] usc.es
- Category
- Xunta Pre-doctoral Contract
| Tuesday | |||
|---|---|---|---|
| 17:30-20:00 | Grupo /CLIL_01 | Galician, Spanish | IA.11 |
| Wednesday | |||
| 17:30-20:00 | Grupo /CLIL_03 | Spanish, Galician | IA.S1 |
| Thursday | |||
| 15:30-16:30 | Grupo /CLE_01 | Galician | IA.11 |
| 17:30-20:00 | Grupo /CLIL_02 | Galician, Spanish | IA.13 |
| Friday | |||
| 13:00-14:00 | Grupo /CLE_01 | Galician | IA.S1 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_03 | IA.01 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_01 | IA.01 |
| 06.03.2025 09:00-14:00 | Grupo /CLE_01 | IA.01 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_02 | IA.01 |
| 06.03.2025 09:00-14:00 | Grupo /CLE_01 | IA.11 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_02 | IA.11 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_03 | IA.11 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_01 | IA.11 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_03 | IA.12 |
| 06.03.2025 09:00-14:00 | Grupo /CLE_01 | IA.12 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_01 | IA.12 |
| 06.03.2025 09:00-14:00 | Grupo /CLIL_02 | IA.12 |
| 07.02.2025 09:00-14:00 | Grupo /CLE_01 | IA.11 |
| 07.02.2025 09:00-14:00 | Grupo /CLIL_01 | IA.11 |
| 07.02.2025 09:00-14:00 | Grupo /CLIL_02 | IA.11 |
| 07.02.2025 09:00-14:00 | Grupo /CLIL_03 | IA.11 |