| 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 |
| CT1 | Become sufficiently independent to work on research projects and scientific or technological collaborations within their thematic area. | |
| 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 |
| CT1 |
Manage and communicate complex information in foreign language. | |
| CT2 |
Manage information and knowledge by making efficient use of the information technologies. | |
| CT4 |
Participate in the group in a good working environment and help to solve problems. | |
| 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 |
|
17 | 23.5 | 40.5 | ||||||
| Presentations / oral communications |
|
0.8 | 1.2 | 2 | ||||||
| Assignments |
|
9 | 13.5 | 22.5 | ||||||
| Forums of debate |
|
0.2 | 0.3 | 0.5 | ||||||
| Personal attention |
|
1 | 0 | 1 | ||||||
| Multiple-choice objective tests |
|
1 | 5 | 6 | ||||||
| (*) 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 | |
| Presentations / oral communications | |
| Assignments | |
| Forums of debate | |
| 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 |
|
30% | |||||
| Assignments |
|
Proves pràctiques | 40% | ||||
| Multiple-choice objective tests |
|
Proves tipus test | 30% | ||||
| 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.