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
|
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. |
Type C
|
Code |
Competences Nuclear |
Type A
|
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
|
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 |
PDDL language
Method STRIPS
Linear Planners
Graphplan
MDP
Reinforcement Learning |
Methodologies :: Tests |
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Competences |
(*) Class hours
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Hours outside the classroom
|
(**) Total hours |
Introductory activities |
|
1 |
1.5 |
2.5 |
IT-based practicals in computer rooms |
|
10 |
15 |
25 |
Lecture |
|
14 |
24.5 |
38.5 |
Personal attention |
|
1 |
0 |
1 |
|
Short-answer objective tests |
|
4 |
4 |
8 |
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(*) 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. |
IT-based practicals in computer rooms |
Practical session of the course, implementing the studied algorithms and methods. |
Lecture |
Presentation and explanation of the contents. |
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. |
Description |
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. |
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
IT-based practicals in computer rooms |
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Practical exercises on the topics of the course. |
40% |
Short-answer objective tests |
|
Questions and exercises on the contents of the course.
Two exams must be correctly done.
|
60% |
Others |
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|
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Other comments and second exam session |
A second call will be available for failed evaluation activities. During the exams, no communication or data transmission device can be used. |
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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