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Type A
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Code |
Competences Specific | | | A9 |
Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
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Type B
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Code |
Competences Transversal | | | CT2 |
Forming opinions on the basis of the efficient management and use of information |
| | CT4 |
Work in multidisciplinary teams and in complex contexts. |
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Type C
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Code |
Competences Nuclear |
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Type A
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Code |
Learning outcomes |
| | A9 |
Represent approximate knowledge and apply inference mechanisms to it to resolve complex problems.
Understand the inherent complexity of automatic planning algorithms and the historical evolution of the different techniques proposed in this field.
Implement complex mechanisms of automatic planning and apply them to solving specific problems.
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Type B
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Code |
Learning outcomes |
| | CT2 |
Master the tools for managing their own identity and activities in a digital environment.
Search for and find information autonomously using criteria of importance, reliability and relevance, which is useful for creating knowledge
Organise information with appropriate tools (online and face-to-face) so that it can be updated, retrieved and processed for re-use in future projects.
Produce information with tools and formats appropriate to the communicative situation and with complete honesty.
Use IT to share and exchange the results of academic and scientific projects in interdisciplinary contexts that seek knowledge transfer.
| | | CT4 |
Understand the team’s objective and identify their role in complex contexts.
Communicate and work with other teams to achieve joint objectives.
Commit and encourage the necessary changes and improvements so that the team can achieve its objectives.
Trust in their own abilities, respect differences and use them to the team’s advantage.
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Type C
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Code |
Learning outcomes |
| Topic |
Sub-topic |
| Expert systems with approximate reasoning |
Probabilistic models
Evidence theory.
Fuzzy logic and reasoning based on fuzzy rules. |
| Introduction to planning methods |
Advanced techniques of planning. |
| Methodologies :: Tests |
| |
Competences |
(*) Class hours
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Hours outside the classroom
|
(**) Total hours |
| Introductory activities |
|
1 |
1.5 |
2.5 |
| Assignments |
|
9 |
13.5 |
22.5 |
| Reading written documents and graphs |
|
15.8 |
25.7 |
41.5 |
| Forums of debate |
|
0.2 |
0.3 |
0.5 |
| Personal attention |
|
1 |
0 |
1 |
| |
| Multiple-choice objective tests |
|
2 |
2 |
4 |
| Oral tests |
|
1 |
2 |
3 |
| |
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies
| |
Description |
| Introductory activities |
Presentation of the topic. |
| Assignments |
Practical session of the course, implementing the studied algorithms and methods. |
| Reading written documents and graphs |
Presentation and explanation of the contents. Students must prepare the subject-matter |
| Forums of debate |
Share questions with the teacher and classmates in the Forum. |
| Personal attention |
The lecturers will have several hours during the week in which students can visit them and present them their questions or doubts concerning the content of the course. If it is considered necessary, some of the teachning hours could be devoted to personalized attention. |
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Description |
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The lecturers will have several hours during the week in which students can visit them and present them their questions or doubts concerning the content of the course. If it is considered necessary, some of the teachning hours could be devoted to personalized attention. |
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Methodologies |
Competences
|
Description |
Weight |
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| Assignments |
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Practical exercises on the topics of the course. |
60% |
| Oral tests |
|
Explain some practical exercise.. A minimum grade of 5 in the interview is required. |
10% |
| Multiple-choice objective tests |
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Questions and exercises about the topics of the course. A minimum qualification is required to pass the course.
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30% |
| Others |
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Other comments and second exam session |
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A second call will be available for failed evaluation activities. |
| Basic |
Russell, Norvig, Artificial Intelligence, a modern approach, Prentice-Hall, 2010
M. Ghallab, D. Nau, P. Traverso, Automated planning: theory and practice, elsevier/Morgan Hauffman, 2004
G. J. Klir, B. Yuan, Fuzzy sets and fuzzy logic: theroy and applications, Prentice-Hall, 1995
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| Complementary |
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(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. |
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