ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 11 Total: 22
Use languages Spanish, Galician, English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: External department linked to the degrees
Areas: Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The course introduces the student to the design, modeling and verification of systems
that interact with their environment responding to strict temporal requirements. Students
will be trained in the handling of synchronous and asynchronous hypotheses by means
of specific implementation languages, showing the differences in concept and
illustrating the advantages and disadvantages in each case, especially with regard to
behavior verification, an inherent characteristic of these systems. The architectures for
applying AI techniques to the design of RTSs will be described, highlighting their
advantages and disadvantages in the case of environments of added complexity such as
dynamic or incompletely specified environments. In short, the aim is to train the student
in the development of operational kernels in which the respect of both stimuli
processing and response generation deadlines are critical, something common in
embedded systems in the automotive, aerospace or defense sectors.
Real-time systems. Determinism and reliability. Parallelism. Synchronous and
asynchronous hypothesis. Implementation languages. Simulation. Behavior verification.
Planning strategies. Architectures.
Basic:
J. Liu, "Real-Time Systems", Prentice-Hall, 2000. ISBN: 978-0130996510
Complementary:
H. Kopetz, "Real-Time Systems", Springer, 2011, 2 Edición. ISBN: 978-1441982360
BASIC AND GENERAL
GC1 - Maintain and extend sound theoretical approaches to enable the introduction and
exploitation of new and advanced technologies in the field of Artificial Intelligence.
GC2 - Successfully address all stages of an Artificial Intelligence project.
CG5 - Work in teams, especially multidisciplinary ones, and be skilled in time
management, people and decision making.
CB6 - Possess and understand knowledge that provides a basis or opportunity to be
original in the development and/or application of ideas, often in a research context.
CB7 - That students know how to apply acquired knowledge and problem-solving skills
in new or unfamiliar environments within broader (or multidisciplinary) contexts related
to their area of study.
CB9 - Students should be able to communicate their conclusions and the knowledge and
rationale behind them to specialized and non-specialized audiences in a clear and
unambiguous manner.
CB10 - That students possess the learning skills that will allow them to continue
studying in a way that will be largely self-directed or autonomous.
TRANSVERSALS
CT3 - Use the basic tools of information and communication technologies (ICT)
necessary for the exercise of their profession and for lifelong learning.
CT7 - Develop the ability to work in interdisciplinary or transdisciplinary teams, to
offer proposals that contribute to sustainable environmental, economic, political and
social development.
CT8 - Value the importance of research, innovation and technological development in
the socioeconomic and cultural progress of society.
CT9 - Have the ability to manage time and resources: develop plans, prioritize
activities, identify critical ones, set deadlines and meet them.
SPECIFIC
CE19 - Knowledge of different fields of application of AI-based technologies and their
ability to offer a differentiating added value.
SC20 - Ability to combine and adapt different techniques, extrapolating knowledge
between different application areas.
SC21 - Knowledge of techniques that facilitate the organization and management of AI
projects in real environments, resource management and task planning in an efficient
way, taking into account concepts of knowledge dissemination and open science.
CE22 - Knowledge of techniques that facilitate the security of data, applications and
communications and their implications in different fields of application of AI.
CE30 - To be able to pose, model and solve problems that require the application of
artificial intelligence methods, techniques and technologies.
The methodology includes the expository method / lecture, laboratory practices,
tutorials, independent work, case studies, project-based learning. It will be carried out
with the following training activities:
1) Problem-based learning, seminars, case studies and projects: these are sessions whose
objective is that students acquire certain skills based on the resolution of exercises, case
studies and projects that require the student to apply the knowledge and skills developed
during the course. These sessions may require the student to present orally the solution
to the problems posed. The work carried out by the students can be done individually or
in work groups.
2) Theory classes: Oral exposition complemented with the use of audiovisual media and
the introduction of some questions directed to the students, with the purpose of
transmitting knowledge and facilitating learning. In addition to the time of oral
exposition by the professor, this formative activity requires the student to dedicate some
time to prepare and review on their own the materials object of the class.
3) Practical laboratory classes: classes dedicated to the development of practical work
involving the resolution of complex problems, and the analysis and design of solutions
that constitute a means for their resolution. This activity may require students to present
their work orally. The work done by the students can be done individually or in work
groups.
The assessment will consist of two parts:
- Final exam, with weighting of 50% of the final grade.
- Evaluation of practical work, with a weighting of 50% of the final grade.
It will be necessary to reach 40% of the score in each part in order to pass the subject.
The grade will be not presented when no practical work or final exam is handed in.
Second opportunity
The evaluation will be carried out with the same criteria described above. A new term will be opened for the delivery of the practical work, in case of not having delivered them in the first opportunity.
A1: Theory classes: 10 classroom hours, 20 hours total dedication.
A2: Practical laboratory classes: 7 classroom hours, 28 hours total dedication.
A3: Problem-based learning, seminars, case studies and projects: 4 classroom hours, 27
hours total dedication.
| Wednesday | |||
|---|---|---|---|
| 15:30-17:00 | Grupo /CLIL_01 | - | IA.02 |
| 06.05.2024 16:00-20:45 | Grupo /CLIL_01 | IA.02 |
| 06.05.2024 16:00-20:45 | Grupo /CLE_01 | IA.02 |
| 07.10.2024 16:00-20:45 | Grupo /CLIL_01 | IA.02 |
| 07.10.2024 16:00-20:45 | Grupo /CLE_01 | IA.02 |