| Competences |
| Type A | 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. | |
| 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 |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A9 |
Understand the practical significance of the efficient representation and acquisition of knowledge within Artificial Intelligence. Are familiar with and understand the historical evolution of the mechanisms of management and representation of knowledge. Are familiar with searching for, understanding and using research articles in a foreign language. | |
| Type B | 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. | |
| Type C | Code | Learning outcomes |
| Contents |
| Topic | Sub-topic |
| 1. Introduction and Concepts | Data, Information and Knowledge; Knowledge Types and Uses; Knowledge Representation; Knowledge Engineering; Syntax and Semantics |
| 2. Knowledge Representation | First order logic; Rules and production systems; Object-Oriented Representations; Network Representation; Ontologies |
| 3. Knowledge Engineering | Knowledge Life-Cycle; Knowledge Audit; Knowledge Acquisition |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 1.5 | 2.5 | ||||||
| Reading written documents and graphs |
|
15 | 45 | 60 | ||||||
| Presentations / oral communications |
|
1 | 8 | 9 | ||||||
| Assignments |
|
9 | 13.5 | 22.5 | ||||||
| Forums of debate |
|
0.2 | 0.3 | 0.5 | ||||||
| Personal attention |
|
1 | 0 | 1 | ||||||
| Mixed tests |
|
6 | 12 | 18 | ||||||
| (*) 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 | Course presentation: contents, calendar of activities, evaluation, bibliography, ... |
| Reading written documents and graphs | Study of the chapters and contents of the course. |
| Presentations / oral communications | Presentation of a video describing the practival work developed. |
| Assignments | One or two practical works. |
| Forums of debate | Participation in forums for topic discussion. |
| Personal attention | Personalized attention to solve student doubts. |
| Personalized attention |
| Description |
| El professor proporciona sis hores a la setmana per atendre els dubtes individuals i de grup |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| Presentations / oral communications |
|
Practical work and presentation | 30% | ||||
| Mixed tests |
|
Two exams | 70% | ||||
| Others |
| Other comments and second exam session | |||
| Sources of information |
| Basic |
Brachman, Ronald J; Levesque, Hector J., Knowledge Representation and Reasoning, 2004, Morgan Kaufmann
Riaño, David, Knowledge Representation and Engineering Notes, yearly, |
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| Complementary | |||||||||
| Recommendations |
(*)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.