Degree | Type | Year | Semester |
---|---|---|---|
2504392 Artificial Intelligence | OB | 2 | 2 |
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
To have taken the courses on Computational Logic, Fundamentals of Programming I, and Fundamentals of Programming II; and to have basic knowledge of the Python programming language.
Representing and reasoning with knowledge in computationally effective ways is an important and fundamental aspect of artificial intelligence. It determines the efficacy and efficiency of computational systems in their capacity for learning and problem solving. This course is an introduction to the main approaches and techniques for representing knowledge in computational systems in order to build knowledge-based systems.
The objective of the course is: to study logic-based and feature-based approaches to knowledge representation, which provide the formal structure and inference mechanisms; to analyse the foundations of ontologies, by which we define the entities that are we think are relevant in particular application domains; and to introduce the main computational techniques that allow the use of knowledge for learning, reasoning, and problem solving. The course also aims at examining the main difficulties of formally representing knowledge in computational systems and of deploying knowledge-based systems in real-world applications, tackling thereby the issues of uncertainty, vagueness, and change.
The course follows a flipped-class methodology, complemented with exercises and practical engineering work.
Students are expected to autonomously complete readings or listen to video lectures on the theoretical contents of the course, so that theoretical lectures can be devoted to clarify and deepen the core theoretical concepts. This is complemented with problem solving seminars in which students are expected to do exercises and solve problems in team work, under supervision. Problems and exercises are either fully solved during problem seminars, or else some hints are given so that students can complete the problems autonomously. Finally, students are to carry out a knowledge engineering project in small teams, during which most of the content of the course is put into practice. In supervised practical sessions, the engineering methodology and development steps are introduced, as well as the necessary skills for using the programming and system development environments. The bulk of the engineering project, however, is carried out autonomously by the student teams.
The Virtual Campus platform (http://cv.uab.cat/) will be used to share teaching materials, deliver the engineering project, consult the marks of the subject, communicate with the teaching staff, etc.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Theoretical lectures | 24 | 0.96 | 8, 5, 6 |
Type: Supervised | |||
Practical sessions on knowledge engineering | 10 | 0.4 | 1, 3, 9, 7, 2, 4 |
Problem solving seminars | 14 | 0.56 | 1, 3, 7, 2, 4 |
Type: Autonomous | |||
Development of knowledge engineering project | 35 | 1.4 | 1, 3, 9, 7, 2, 4 |
Personal study and readings | 35 | 1.4 | 8, 5, 6 |
Problem solving | 24 | 0.96 | 1, 3, 7, 2, 4 |
The evaluation of the level of achievement of the course takes into account the team work done in problem seminars and practical sessions, as well as the scientific and technical knowledge of the subject gained by the students. To carry out this evaluation, the following aspects are taken into account:
Assessment Items and Their Relative Weight Towards the Final Grade
The final grade reflects the overall level of achievement for the course by each student, and it is determined, in the first call, by combining the marks of the various assessment items as follows:
60% of the grade corresponds to theory and problems, broken down as follows:
(a) 30% corresponds to the arithmetic mean of the marks of three written exercises that are carried out and delivered in small teams in the problem solving seminars (i.e., each exercise contributes 10% to the final grade).
(b) 30% corresponds to the mark of the written exam on theory and problems that is carried out individually on the day of the final examination.
40% of the grade corresponds to the knowledge engineering project, broken down as follows:
(c) 30% corresponds to the mark of the knowledge engineering project done in small teams, and based on a delivery and a face-to-face defense of the project, to which the attendance of all members of each team is mandatory.
(d) 10% corresponds to the mark of the written exam on the engineering project, which is carried out individually on the day of the final examination.
To pass the course in the first call, it is mandatory to obtain at least a mark of 5 in each one of the four assessment items (a), (b), (c), and (d).
In the second call it is possible to improve, by means of a written retake exam, the mark corresponding to all theory-and-problems assessment items (items (a) and (b), i.e., 60% of the final grade of the course) and the mark of the written exam on the knowledge engineering project (item (d), i.e., 10% of the final grade).
To pass the course in the second call, it is mandatory to obtain at least a mark of 5 in each one of these two recoverable parts, in addition to having passed, with a minimum of 5, the knowledge engineering project done in teams (which is evaluated in one of the practical sessions, and therefore cannot be recovered in the second call).
Planning of Assessment Activities
Continuous-assessment dates such as problem-solving exercises and project presentations will be made public on the Virtual Campus and on lecture slides. Dates may change when necessary. Any such modification will always be communicated to students in class and through the Virtual Campus within the schedule set by the centre or degree programme.
Non-Assessment
The final grade of the student will be 'ABSENT' as long as the student has not been assessed more than 30% of the subject.
Honors
Awarding an honors degree is the decision of the teaching staff responsible for the subject. UAB regulations dictate that Honors can only be granted to students who have obtained a final grade equal to or greater than 9, and that only up to 5% of the total number of students enrolled can be awarded an Honors degree.
Plagiarism
Without prejudice to others that are deemed appropriate and in accordance with current academic legislation, irregularities committed by a student during an assessment activity can lead to a change of mark to 0. Assessment activities marked in this way and by this procedure will not be recoverable. If it is necessary to pass any of these assessment activities to pass the course, the student will not pass the course, with no opportunity to recover it in a second call in the same academic year. These irregularities include, among others:
In case the student has committed irregularities in any assessment activity (and therefore it will not be possible to pass the course in a second call), the final grade of the course will be the lowest of the value 3 and the weighted average of the marks. In summary: copying, let others copy your work or plagiarising in any of the assessment activities is equivalent to a failure with a grade lower than or equal to 3.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exam on the knowledge engineering project, individual | 10% | 1 | 0.04 | 1, 8, 5, 7, 4 |
Exam on theory and problems, individual | 30% | 2 | 0.08 | 1, 8, 5, 6, 7, 2, 4 |
Knowledge engineering project, in teams | 30% | 2 | 0.08 | 1, 8, 3, 9, 5, 7, 4 |
Problem-solving exercise, in teams (1) | 10% | 1 | 0.04 | 1, 8, 3, 9, 7 |
Problem-solving exercise, in teams (2) | 10% | 1 | 0.04 | 1, 8, 3, 9, 7, 4 |
Problem-solving exercise, in teams (3) | 10% | 1 | 0.04 | 1, 8, 3, 6, 2, 4 |
The knowledge engineering project will make use of freely available and downloadable programming and development tools available for the main operating systems (Windows, macOS, and Linux) such as the ontology editor Protégé (https://protege.stanford.edu/) and the programming language Python (https://www.python.org/). They will be introduced during the practical engineering sessions.