| 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 | ||
| Conocer los conceptos fundamentales de la lógica de predicados | AC5 AC6 AC7 AC8 |
BC1 BC3 BC6 BC7 BC8 BC9 |
CC2 CC3 CC4 CC7 CC8 |
| Knowing and undertanding the concepts of imprecision and uncertainty versus certainty | AC5 AC6 AC7 AC8 |
BC1 BC3 BC6 BC7 BC8 BC9 |
CC2 CC3 CC5 CC8 |
| Knowing the main imprecise reasoning models and how to apply them to problem solving in AI | AC5 AC6 AC7 AC8 |
BC1 BC2 BC3 BC6 BC7 BC8 BC9 |
CC2 CC3 CC4 CC5 CC6 CC7 CC8 |
| Knowing how to model and solve basic planning problems | AC5 AC6 AC7 AC8 |
BC1 BC2 BC3 BC6 BC7 BC8 BC9 |
CC2 CC3 CC4 CC5 CC7 CC8 |
| Contents |
| Topic | Sub-topic |
| Knowledge Representation | Knowledge Representation |
| Formal logic | Formal Logic and human thinking |
| Automated Reasoning | Models and techniques of Automated Reasoning |
| Uncertainty | Paradigms for reasoning with imprecision and uncertainty |
| Planning | Automated planning, and planning under uncertainty Planning |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A6 A7 A8 A9 B2 B3 B6 B8 B9 C2 C6 | 21 | 42 | 63 |
| Objective test | A6 A7 A8 A9 B3 B6 B7 B8 B9 C2 | 3 | 21 | 24 |
| Laboratory practice | A6 A7 A8 A9 B1 B2 B3 B7 B8 C3 C4 C5 C6 C7 C8 | 21 | 42 | 63 |
| 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 |
| Guest lecture / keynote speech | Classes of concepts and foundations with small exercises |
| Objective test | Individual exam |
| Laboratory practice | Practical work, normally in groups, with tools of reasoning and planning |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Laboratory practice | A6 A7 A8 A9 B1 B2 B3 B7 B8 C3 C4 C5 C6 C7 C8 | Submission of one or several practical assignments | 49.5 |
| Guest lecture / keynote speech | A6 A7 A8 A9 B2 B3 B6 B8 B9 C2 C6 | Depending on how the course evolves, a part of the exam could be consolidated by submitting solved exercises along the lecture classes period | 0.5 |
| Objective test | A6 A7 A8 A9 B3 B6 B7 B8 B9 C2 | An individual exam consisting of several exercises that will be assessed up to a maximum of 50 points. *Requirement* a minimum grade of 20 points in the exam must be achieved to pass the course. If that minimum grade inside the exam is not achieved, the final total grade for the course will be truncated to 4.8 (that is 48%) if the addition of all qualifications are above that number. |
50 |
| Assessment comments | |||
| Sources of information |
| Basic | |
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| Complementary |
Vladimir Lifschitz (2019). Answer Set Programming. Springer
Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub (2012). Answer Set Solving in Practice. Morgan and Claypool Publishers
Stuart Russell and Peter Norvig (2021). Artificial Intelligence: a Modern Approach (4th ed). Pearson, Prentice Hall
Chitta Baral (2003). Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press
Michael Gelfond and Yulia Kahl (2014). Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press |
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| Recommendations | |||||||||
| Subjects that it is recommended to have taken before |
| Subjects that are recommended to be taken simultaneously | |
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