Knowledge and Reasoning under Uncertainty

The subject deals with some of the most important paradigms for the modelling of knowledge with uncertainty and the reasoning about this type of models. Firstly, we will deal with graphical representation models, which allow to simplify the analysis of any probabilistic model. An introduction to decision theory and decision networks will be made, which, combined with probability theory, allows choosing the optimal alternative from the available information, whether this is incomplete or ambiguous. Finally, the fuzzy paradigm is treated as a basis for the modeling of imprecise vocabularies and computation with words, and the execution of different types of approximate reasoning.

Learning outcomes: To know the main models of imprecise reasoning to evaluate their suitability for problem solving in the field of Artificial Intelligence.

1. Graphic models.
2. Bayesian networks.
3. Exact and approximate inference in graphical models.
4. Utility Theory.
5. Decision networks.
6. Computation with words and fuzzy reasoning models.

Basic Bibliography
- S. Russell, P. Norvig. Artificial Intelligence. A Modern Approach. 4th ed. Pearson, 2022.
- R. Marín, J.T. Palma (Eds.) Inteligencia Artificial y Sistemas Inteligentes. Ed. McGraw-Hill, 2008.
- K.B. Korb, A.E. Nicholson, Bayesian Artificial Intelligence. 2nd Ed. Chapman&Hall/CRC, 2011.
- Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
- E. Trillas, L. Eciolaza, Fuzzy Logic: An Introductory Course for Engineering Students, Springer 2015
- M. J. Kochenderfer, T. A. Wheeler, K. H. Wray. Algorithms for Decision Making, MIT Press, 2022.

Supplementary Bibliography
- J.M. Mendel, Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83, 3, 345-377, 1995.
- A. Darwiche, Modelling and reasoning with Bayesian networks. Cambridge Univ. Press, 2009.
- Pearl, J., Probabilistic Reasoning in Intelligent Systems: networks of plausible inference. Morgan-Kaufmann, 1988.
- Curso de Inteligencia Artificial. Fernando Sancho Caparrini. http://www.cs.us.es/~fsancho

The course contributes to the development of the following general and specific competences included in the degree report:

BASIC AND GENERAL
CG1 - Maintain and extend theoretical formulations founded to allow the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
CG3 - Search and select useful information needed to solve complex problems, handling with fluency the bibliographic sources of the field.
CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, especially in a research context.
CB7 - That students are able to apply the acquired knowledge and their problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.

TRANSVERSAL
CT2 - Master the oral and written expression and comprehension of a foreign language.

SPECIFIC
CE5 - Ability to design and develop intelligent systems through the application of inference algorithms, knowledge representation and automatic planning.

The course will use the Virtual Campus of the three universities as the basic platform (contents repository and virtual tutoring of students). In the virtual class of the course students will have access to all the materials (theoretical materials, class slides, labs scripts, ...). The didactic methodology will be based essentially on individual work, although sometimes it will be developed in groups, mainly in discussion with the teaching staff in expository and interactive classes.

Theoretical sessions (face to face at USC, streamed online for UdC and UVIGO students): for each topic or thematic block of the lectures, the teacher will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliographical resources and provide additional work material, mainly exercises related to the theoretical concepts. In addition, the teacher will propose to the students a set of activities to be carried out individually or in groups (cases, exercises) that the students must submit for evaluation, according to the deadlines.
The interactive sessions will take place face to face in the IT lab, using different software tools and developing applications for each of the thematic blocks.
Students will work individually or in small groups, with constant monitoring and tutoring from the teacher. Practice scripts will be provided with the tasks to be carried out individually or in small groups.

The assessment of learning considers both the theoretical and the practical part. In order to pass the subject an overall mark equal to or higher than 5 must be achieved, out of a maximum score of 10 points in the planned assessment activities, whose weight in the final assessment will be within the ranges included in the degree report:

E1: Final exam 50%
E2: Assessment of practical work 50%

Obterán a cualificación de no presentado os/as estudantes que no se presentaron ao exame nin se someterán á avaliación de ningunha outra actividade obrigatoria.
In order to pass the subject at the second opportunity, students must submit to the evaluation of all those parts or compulsory deliveries pending that are established. For the rest will keep the qualifications obtained during the course.
In case of fraudulent realization of exercises or tests, will apply the recolleito in the rules of evaluation of the academic performance of students and review of qualifications ( https://www.xunta.gal/dog/publicados/2011/20110721/ AnuncioG2018-190711-4180_ gl.html). In application of the regulations of the ETSE on plaxio (approved by the Xunta dá ETSE on 19/12/2019), the total or partial copy of any exercise of practices or theory will suppose the suspense of the two opportunities of the course, with qualification of 0,0 in both cases (https://www.usc.es/etse/files/u1/ NormativaPlagioETSE2019.pdf).

Classroom work time: 21 hours, divided into 10 hours (theory classes), 7 hours (practical laboratory classes), 4 hours (problem-based learning, seminars, case studies and projects).
Tempo de traballo persoal: 54 h (total).

We recommend students to solve, implement, verify and validate all the proposed exercises and practices (not only the assessable ones). It is also important to make an intense use of the tutorials for the resolution of doubts and an active participation in the expository and interactive sessions.

This course will be taught in English.
The theoretical classes will be delivered by USC, face to face for USC students and streamed online for UdC and UVIGO students.
Interactive classes at USC will also be face to face in an IT lab.