Artificial Intelligence

Academic year
Subject Area
Intelligent Systems
Language of Instruction
Other Languages of Instruction
Mode of Delivery
ECTS Credits
2nd Cycle Studies - Mestrado

Recommended Prerequisites

Programming competences. Introductory course on Artificial Intelligence (recommended).

Teaching Methods

A Project Based Learning approach is adopted, directed towards competence acquisition through the development of a laboratory work (project) with a high research component, demanding the combination of theoretical concepts and promotes critical reasoning over complex problems. The work comprises the writing of a scientific article, describing the work done, as well as its presentation and defense.

Theoretical classes will comprise detailed presentation of Artificial Intelligence concepts, principles and fundamental theories.

Learning Outcomes

To provide the students with advanced concepts, principles and theories required for building real world applications with agents or systems that can reason, behave or interact with their environment in an intelligent way by learning and reasoning about the real world.

Acquiring competencies in synthesis and analysis, organization and planning, written communication, problem solving, decision-making, team work, critical reasoning, autonomous learning, practical application of theoretical knowledge, and research.

Work Placement(s)



1.Logic for Knowledge Representation and Reasoning

•Logical formalisms for knowledge representation: propositional logic, first order predicate logic; default logic; descriptive logic; ontologies and taxonomies;

•Logical reasoning: satisfiability, credulous and skeptical entailment

•Logic Programming


2.Autonomous Agents and Multi-Agent Systems

•Agents and environments; taxonomy of agents

•BDI Architecture: beliefs, desires and intentions;

•Emotions and affective computing agents

•Establishing agreements: negotiation and argumentation

•Working together: cooperation and coordination

•Agent-oriented software engineering

•FIPA Standards


3.Knowledge and Reasoning with Uncertainty

•Quantification of uncertainty

•Approaches to reasoning with uncertainty: probabilistic reasoning and default logic

•Probabilistic reasoning over time

•Decision: single decisions, sequential decisions / planning, planning under uncertainty


Head Lecturer(s)

Luís Miguel Machado Lopes Macedo

Assessment Methods

Exam: 40.0%
Laboratory work or Field work: 60.0%


- Wooldridge, Michael. An introduction to MultiAgent Systems, 2nd. Edition, John Wiley, 2009.

- Russell, Stuart, and Norvig, Peter. Artificial Intelligence: a Modern Approach, 3rd. Edition, Prentice Hall, 2010.

- Chitta Baral, "Knowledge Representation, Reasoning and Declarative Problem Solving", Cambridge University Press, 2003

- "The Description Logic Handbook: Theory, Implementation and Applications, 2nd Edition", Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, Peter F. Patel-Schneider, Cambridge University Press, 2007

- L Sterling and E Shapiro, "The Art of Prolog: Advanced Programming Techniques (Logic Programming) (2nd ed.)", MIT Press, 1994

- I Bratko, "Prolog Programming for Artificial Intelligence (3rd ed.)", Addison-Wesley, 2001