Advanced Topics in Artificial Intelligence

Recommended Prerequisites

Basic knowledge on programming and artificial intelligence

Teaching Methods

Classes with detailed exposition, using visual aids, of the concepts, principles and fundamental theories, together with the resolution of practical exercises that arouse students’ interest in those subjects and exemplify their application to real situations.

Learning Outcomes

To provide the students with advanced concepts, principles and theories required for building 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, critical reasoning, autonomous learning, practical application of theoretical knowledge, and research.

Work Placement(s)

No

Syllabus

1. Autonomous Agents and Multi-agent Systems
Introduction to agents and environments; agents’ taxonomies; beliefs, desires, intentions, and emotions; communication; learning; reaching agreements: negotiation, argumentation; working together: cooperation and coordination; agent-oriented software engineering; robotics; applications.
2. Knowledge Representation and Reasoning – Computational Logic
Introduction to computational logic; overview of logic-based knowledge representation formalisms and reasoning; classical logic (propositional logic, first order predicate calculus); non-monotonic logic; hybrid knowledge representation systems
3. Uncertain Knowledge and Reasoning
Quantifying uncertainty; probabilistic reasoning; probabilistic reasoning over time; single decisions; sequential decisions/ planning under uncertainty; applications.
4. Machine Learning
Forms of learning; performance measurement; learning with decision trees; learning bayesian networks; instance/memory-based learning.

Head Lecturer(s)

Luís Miguel Machado Lopes Macedo

Assessment Methods

Assessment
Laboratory work or Field work: 50.0%
Written work concerning a survey on a topic of Artificial Intelligence: 50.0%

Bibliography

- 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.
- Baral, Chitta. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, 2003.
- F. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and Patel-Schneider, P. F. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, 2003.
- Antoniou, Grigoris. Nonmonotonic Reasoning. MIT Press, 1996.