| Study programme competencies |
| Code | Study programme competences |
| A6 | CE05 - Ability to design and develop intelligent systems through the application of inference algorithms, knowledge representation and automated planning |
| A7 | CE06 - Ability to recognise those problems that require a distributed architecture, not predetermined during the system design, suitable for the implementation of multiagent systems |
| A8 | CE07 - Ability to understand the consequences of the development of an explainable and interpretable intelligent system |
| A9 | CE08 - Ability to design and develop secure intelligent systems, in terms of integrity, confidentiality and robustness |
| B1 | CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI |
| B2 | CG02 - Successfully addressing each and every stage of an AI project |
| B3 | CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B6 | CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 | CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B8 | CB03 - The students will be able to integrate different pieces of knowledge, to face the complexity of formulating opinions (from information that may be incomplete or limited) and to include considerations about social and ethical responsibilities linked to the application of their knowledge and opinions |
| B9 | CB04 - The students will be able to communicate their conclusions, their premises and their ultimate justifications, both to specialised and non-specialised audiences, using a clear style language, free from ambiguities |
| C2 | CT02 - Command in understanding and expression, both in oral and written forms, of a foreign language |
| C3 | CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C4 | CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective |
| C5 | CT05 - Understanding the importance of the entrepreneurial culture and knowledge of the resources within the entrepreneur person's means |
| C6 | CT06 - Acquiring abilities for life and healthy customs, routines and life styles |
| C7 | CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 | CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Introduce the concept of multi-agent systems based on the need for distributed architectures in intelligent systems | AC6 AC7 AC8 |
BC1 BC9 |
CC3 CC6 CC8 |
| Understand the different approaches to intelligent agent architectures | AC5 AC6 |
BC1 BC6 BC7 |
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| Understand the notion of negotiation as a simple aspect inherent to multi-agent systems. | AC6 AC7 |
BC6 BC7 |
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| Understand the notions and basic aspects of communication, coordination and cooperation. | AC6 AC7 |
BC8 |
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| Analyze the various existing methodologies for the development of multi-agent systems. | AC5 AC6 |
BC2 BC8 |
CC2 |
| Know applications of this type of systems in industrial, medical, computer environments, etc. | AC6 |
BC3 BC6 BC7 |
CC4 CC5 CC7 |
| Contents |
| Topic | Sub-topic |
| Introduction | Intelligent agent concept Multiagent system |
| Agent architectures |
Deliberative architectures Reactive architectures Hybrid architectures |
| Interaction between agents | Communication Negotiation Cooperation Coordination |
| Agent-oriented methodologies | Adaptation of existing methodologies Agent-oriented methodologies |
| Applications | Industry Medicine Computer science |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Laboratory practice | A6 A9 B2 C3 C6 C7 | 14 | 48 | 62 |
| Problem solving | A7 B1 B3 B7 C4 C5 | 7 | 39 | 46 |
| Oral presentation | B9 C2 | 1 | 1 | 2 |
| Guest lecture / keynote speech | A8 B8 C8 | 21 | 17 | 38 |
| Objective test | B6 B8 C2 | 2 | 0 | 2 |
| Personalized attention | 0 | 0 | ||
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Laboratory practice | The practical classes will consist of developing a basic multiagent system (MAS) or some specific parts of it. The delivery may have different deadlines to encourage continuous work. The practical instructions will be provided in advance for students to read in detail, and they must be strictly followed. Later, the work of the teachers will be to supervise the practical sessions, resolving doubts and correcting misinterpretations, errors, etc |
| Problem solving | In the problem classes, practical assumptions will be presented directly related to theoretical concepts. The students will have to look for alternative solutions outside the classroom. The aim is to encourage student participation and promote, as far as possible, open dialogue and the assessment of solutions. |
| Oral presentation | For some practical or problem, students must prepare a presentation where they expose their work in the classroom, highlighting the main contributions and conclusions. |
| Guest lecture / keynote speech | Oral presentation supplemented with the use of audiovisual media and introduction of some questions addressed to students for the purpose to transmit knowledge and facilitate learning. |
| Objective test | It will consist of theoretical-practical questions about any of the concepts included in the course agenda. |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Objective test | B6 B8 C2 | It will consist of theoretical and practical questions on any of the items included in the contents | 40 |
| Laboratory practice | A6 A9 B2 C3 C6 C7 | Realization of the tasks, in time and form, is established in the instructions of any proposed practical. To pass the subject is essential to have made and approved the practicals. As part of it, issues such as school attendance, personal work, attitude, etc. will help to pass the practicals. | 60 |
| Oral presentation | B9 C2 | It could be included in some problem solving/laboratory practice and it would affect the final grade of it, however it is not graded on its own. | 0 |
| Assessment comments | |||
| Sources of information |
| Basic |
Michael Wooldridge (2009). An introduction to multiagent systems. Wiley
Adelinde M. Uhrmacher, Danny Weyns (2009). Multi-Agent Systems Simulation and Applications. Routledge, Taylor & Francis Group
Gerhard Weiss (2013). Multiagent Systems, Second Edition. MIT Press |
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| Complementary | |
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| Recommendations | ||
| Subjects that it is recommended to have taken before | ||
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| Subjects that are recommended to be taken simultaneously |
| Subjects that continue the syllabus |
| Other comments | |