| Competencies / Study results |
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Code
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Study programme competences / results
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| A12 |
Conocer los fundamentos de los algoritmos de la inteligencia artificial y la optimización, entender su complejidad computacional y saber aplicarlos a la resolución de problemas. |
| B2 |
Que el alumnado sepa aplicar sus conocimientos a su trabajo o vocación de una forma profesional y posea las competencias que suelen demostrarse por medio de la elaboración y defensa de argumentos y la resolución de problemas dentro de su área de estudio. |
| B4 |
Que el alumnado pueda transmitir información, ideas, problemas y soluciones a un público tanto especializado como no especializado. |
| B8 |
Capacidad para diseñar y crear modelos y soluciones de calidad basadas en Inteligencia Artificial que sean eficientes, robustas, transparentes y responsables. |
| B9 |
Capacidad para seleccionar y justificar los métodos y técnicas adecuadas para resolver un problema concreto, o para desarrollar y proponer nuevos métodos basados en inteligencia artificial. |
| B10 |
Capacidad para concebir nuevos sistemas computacionales y/o evaluar el rendimiento de sistemas existentes, que integren modelos y técnicas de inteligencia artificial. |
| C1 |
Capacidad para comunicar y transmitir sus conocimientos, habilidades y destrezas. |
| C3 |
Capacidad para crear nuevos modelos y soluciones de forma autónoma y creativa, adaptándose a nuevas situaciones. Iniciativa y espíritu emprendedor. |
| C5 |
Capacidad para desarrollar modelos, técnicas y soluciones basadas en inteligencia artificial que resulten éticas, no discriminatorias y confiables. |
| Learning aims |
| Learning outcomes |
Study programme competences / results |
| Apply and implement search methods with informed and uninformed strategies in problems represented as state spaces. |
A12
|
B2
B9
B10
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C5
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| Know different problem solving algorithms based on the search in a space of possible configurations. |
A12
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B2
B8
B9
B10
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C1
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| Know how to solve adversarial search problems |
A12
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B2
B8
B9
B10
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C1
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| Know how to solve search and optimisation problems with constraints. |
A12
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B2
B4
B8
B9
B10
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C1
C3
C5
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| Know and know how to model and solve basic planning problems. |
A12
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B2
B8
B9
B10
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C1
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| Contents |
| Topic |
Sub-topic |
| Topic 1.- Introduction. |
What is AI?
Brief history.
Risks and benefits.
Intelligent agents: rationality and types.
Types of environments. |
| Topic 2.- Problem solving by means of search |
Uninformed search algorithms: breadth, uniform cost, depth, bidirectional and variants.
Informed search algorithms (greedy search, A* algorithm, memory-constrained search).
Heuristic functions |
| Topic 3.- Constraint satisfaction problems. |
Definition, variants
Inference in constraint propagation
Backtracking search
Local search |
| Topic 4.- Automatic planning |
Classical planning.
Algorithms and Heuristics.
Hierarchical planning and searches.
Planning in non-deterministic domains.
Time, ordering, resources
Analysis of planning approaches |
| Topic 5.- Searching in complex environments |
Local search algorithms (hill climbing, simulated cooling, evolutionary algorithms).
Search with non-deterministic actions.
Search in partially observable environments.
Online search. |
| Topic 6.- Adversarial search and games |
Game theory.
Optimal decisions in games.
Heuristic search alpha-beta trees.
Monte Carlo tree search.
Stochastic games.
Partially observable games.
Limitations of the algorithms. |
| Planning |
| Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
| Supervised projects |
A12 C1 |
8 |
15 |
23 |
| Guest lecture / keynote speech |
A12 B2 B4 B8 B9 B10 |
30 |
37 |
67 |
| Mixed objective/subjective test |
A12 B2 B4 B8 B9 B10 C1 C3 C5 |
2 |
14 |
16 |
| Laboratory practice |
B2 B8 B9 B10 C3 C5 |
22 |
12 |
34 |
| |
| Personalized attention |
|
10 |
0 |
10 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
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Methodologies |
Description |
| Supervised projects |
Work will be carried out on different aspects of theoretical interest of the subject. Students will analyse real problems that show the application of the algorithms and techniques described in the theory classes. |
| Guest lecture / keynote speech |
Used during the theoretical classes to expose a basic core of knowledge that students will later have to know how to use and expand in their laboratory practices and in the carrying out of tutored work |
| Mixed objective/subjective test |
There will be a test at the end of the term on the contents covered throughout the course. |
| Laboratory practice |
Students will tackle practical work related to the development and application of different search and planning algorithms. |
| Personalized attention |
|
Methodologies
|
| Guest lecture / keynote speech |
| Laboratory practice |
| Supervised projects |
|
| Description |
| Desenvolverase unha atención personalizada para as prácticas de aula e o traballo supervisado |
|
| Assessment |
|
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
| Mixed objective/subjective test |
A12 B2 B4 B8 B9 B10 C1 C3 C5 |
It makes up 50% of the mark. It will not be possible to pass the subject if a mark lower than 4.5 is obtained in this section. |
50 |
| Laboratory practice |
B2 B8 B9 B10 C3 C5 |
Active and continuous work during the practical classes will be taken into account in its assessment. It constitutes 40% of the mark. It will not be possible to pass the subject if the final mark for the practical classes is lower than 4.5. |
40 |
| Supervised projects |
A12 C1 |
It constitutes 10% of the final grade. |
10 |
| |
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Assessment comments |
All aspects related to ‘academic dispensation’, ‘dedication to study’, ‘permanence’ and ‘academic fraud’ will be governed in accordance with the current academic regulations of the UDC. |
| Sources of information |
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Basic
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Stuart Russel and Peter Norvig. Artificial Intelligence. A modern approach. 4 edición,2021. Moret et al. Fundamentos de Intelixencia Artificial. Servizo de publicacións da UDC. J.T. Palma, R. Marín Morales. Inteligencia Artificial, Técnicas, métodos y aplicaciones,McGraw Hill, 2008 |
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Complementary
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| Recommendations |
| Subjects that it is recommended to have taken before |
| Programming I/614G03006 | | Programming II/614G03007 | | Algorithms/614G03008 | | Mathematical Optimisation/614G03005 |
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| Subjects that are recommended to be taken simultaneously |
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| Subjects that continue the syllabus |
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| Other comments |
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The development of a critical, open and respectful citizenship with the diversity of our society will be encouraged, highlighting the equal rights of students without discrimination based on gender or sexual condition. Inclusive language will be used in the material and development of classes. We will work to identify and modify sexist prejudices and attitudes and will influence the environment to modify them and promote values of respect and equality. The full integration of students who, for physical, mental or socio-cultural reasons, experience difficulties in gaining adequate, equal and beneficial access to university life will be facilitated. |
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