Competencies and objectives
Course context for academic year 2024-25
Located in the first semester of the Master's in Artificial Intelligence, this course provides a theoretical and practical introduction to the fundamental concepts of intelligent agents and multi-agent systems. The practical focus of the course will also concentrate on Agent-Based Modeling (ABM).
Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25
Transversal Competences
- CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
- CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
- CT3 : Show oral and written communication skills.
General Competences
- CG1 : Apply the knowledge acquired to real problems related to artificial intelligence.
- CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
- CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
- CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.
- CG8 : Knowing how to plan, design, develop, implement and maintain products, applications and services related to artificial intelligence, taking into account technical, economic and efficiency aspects.
Specific Competences
- CE04 : Select and apply the appropriate multi-agent system architecture to solve a given problem.
- CE05 : Design and implement multi-agent systems taking into account their cognitive, coordination and communication capabilities.
Basic Competences
- CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
- 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 the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
- CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
- CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.
Learning outcomes (Training objectives)
- Compare and contrast different agent and organisational architectures.
- Understand the cognitive, communication and coordination capabilities of multi-agent systems.
- Design multi-agent system applications using distributed computing and simulation tools.
- Correctly identify the applicability of different types of intelligent agent systems in wider, multidisciplinary contexts and the benefits they bring to these contexts.
Specific objectives stated by the academic staff for academic year 2024-25
1. Understand the concept of multi-agent systems and differentiate it from other fields.
2. Understand and apply BDI (Belief-Desire-Intention) models.
3. Discern the importance and use of communication and coordination in multi-agent systems.
4. Become familiar with the fundamentals of agent-based modeling (ABM) and its utility.
5. Acquire skill in using a practical platform to implement and experiment with agent-based models.
6. Know and understand the role of learning in multi-agent systems.
7. Explore and acquire an understanding of various nature-inspired algorithms.
8. Learn to analyze and evaluate agent-based models through practical examples.
General
Code:
43501
Lecturer responsible:
Fraile Beneyto, Raúl
Credits ECTS:
4,50
Theoretical credits:
0,90
Practical credits:
0,90
Distance-base hours:
2,70
Departments involved
-
Dept:
SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
Area: CIENCIA DE LA COMPUTACIO, INTEL·LIGENCIA ARTIFICIA
Theoretical credits: 0,9
Practical credits: 0,9
This Dept. is responsible for the course.
This Dept. is responsible for the final mark record.
Study programmes where this course is taught
-
UNIVERSITY MASTER'S DEGREE IN ARTIFICIAL INTELLIGENCE
Course type: COMPULSORY (Year: 1)