Objectives of Programme's Learning Outcomes

• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Lead a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
• Demonstrate independence and their ability to work alone or in teams.
• Manage research, development and innovation.
• Organise and lead a research, development or innovation project to completion.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Where possible, communicate in a foreign language.
• Develop and integrate a high degree of autonomy.
• Aquire new knowledge independently.
• Pursue further training and develop new skills independently.
• Develop and integrate a high degree of autonomy to evolve in new contexts.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

Knowledge Representation & Reasoning (KR&R) is a branch of Artificial Intelligence which uses logic languages for (a) representing information and knowledge, and (b) automatic reasoning on top of these representations. In this course, students will get acquainted with some recent technologies developed in the domain of KR&R, and will develop competencies that enable them to represent and solve computational problems by using the most appropriate logic formalism for the problem at hand. 

Content of UE

This course will focus on the following two applications of KR&R in particular:
(1) KR&R as the engine driving the Semantic Web, which is based on the formalism of Description Logic (DL) and implemented in the  W3C Web Ontology Language (OWL).
(2) KR&R for representing and solving problems in the complexity class NP (including all NP-complete problems). This application is based on the formalism known as Answer Set Programming (ASP).

Prior Experience

Students should be familiar with the foundations of propostional and first-order logic (which are taught, for example, in the courses Bases de Données I and II).

Type of Assessment for UE in Q2

• Presentation and/or works
• Written examination

Q2 UE Assessment Comments

The weight of the personal assignment can vary between 50% and 100% of the final mark; the weight that is most favorable to the student will be applied.

Type of Assessment for UE in Q3

• Presentation and/or works
• Written examination

Q3 UE Assessment Comments

The weight of the personal assignment can vary between 50% and 100% of the final mark; the weight that is most favorable to the student will be applied.