Teaching Methodologies
Exhibition classes and discussion classes are given about 60% of the semester. For one or two of the classes, external speakers can be invited to talk about some particular topics. the remaining classes are asasked to a practical component, where students must investigate and develop one or more intelligent agents on topics to choose from within the curriculum program. Students are expected to spend about 40 hours on research and implementation in the practical component.
Learning Results
The student must understand the understandings of intelligence, intelligent agent and the main architectures. You should be able to design, design, and implement a standalone intelligent agent capable of solving common problems using the techniques seized.
Program
Knowledge of natural and artificial/ computational intelligence. The origins of AI and historical debates. Characterization of environments and agents. Learning paradigms. Neuronal networks. Genetic algorithms and adaptive agents. troubleshooting by search. Natural language processing. Robotics.
Internship(s)
NAO
Bibliography
”Artificial Intelligence – A modern Approach”, Stuart Russel and Peter Norvig, Prentice Hall.
“Inteligência Artificial – Fundamentos e Aplicações”, Ernesto Costa e Anabela Simões, FCA.