Class Introduction to Artificial Intelligence

This course aims to confer initial competences in the field of Artificial Intelligence, providing students with solid and structured knowledge that allows them to understand theoretical concepts and develop code for solving practical problems in IIA.

Not applicable

AI basics for some domains. Path search, orientation and navigation: graphs, Dijkstra and A * algorithms, navigation grids and meshes, cost functions. Decisions: decision trees, state machines, behavior trees, other approaches. Learning: Basics, action prediction, Naive Bayes Classifiers, other approaches. Board games: basics, family of minimax algorithms, MCTS, other approaches.

Lecturing consists of theoretical and practical classes. The theoretical component is essentially expository, the theory being presented together with concrete examples. In the practical component, practical programming problems related to the theory taught are developed and solved. In this course unit the evaluation includes the following elements: Theoretical assessment, in the form of written test, exercises, with a weight of 30% in the final grade (minimum grade: 9.5 points). Practical assessment (projects / programming problems / presentations), with a weight of 70% in the final grade (minimum grade: 9.5 points).

Russel, Stuart; Norvig, Peter: Inteligência Artificial: Uma abordagem Moderna. Tradução da 3ª. Campus Editora. 2013 Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans, Pelikan Book, 2019