Overall objectives:

1 - Understanding what characterizes and distinguishes the AI and its applicability.
2 - Knowing how to Represent Knowledge and how to use different methods of Logic and Reasoning.
3 - Using Heuristic Processes versus Systematic for solving problems.
4 - Learning Methods for Solving Problems involving Knowledge.
5 - An ability to design and conduct experiments, as well as to analyze and interpret data [ABET, 3a] 20%
6 - Ability to design and conduct experiments, analyze and interpret data [ABET, 3b] 10%
7 - An ability to design a system, component, or process to meet desired needs [ABET, 3c] 20%
8 - An ability to identify, formulate, and solve engineering [ABET, 3e] 20%
9 - An ability to communicate effectively orally and in writing [ABET, 3g] 20%
10 - Usability techniques and modern tools necessary for engineering practice and a recognition of the need for, and an ability to engage in life-long learning [ABET, 3i, k] 10%

Syllabus:

00 - The topics exceed the discipline contents. Therefore a selection among topics 7 - 11 are made each semester).
1 - Introduction
2 - Intelligent Agents
3 - Solving Problems by Searching
4 - Informed Search and Exploration
5 - Adversarial Search
6 - Knowledge Representation.
7 - Logical Agents
8 - Planning
9 - Multiagent Systems
10 - Machine Learning
11 - Philosophical Foundations

Literature/Sources:

Woolridge, M. , 2001 , Introduction to Multiagent Systems, 2nd ed. , John Wiley & Sons
Costa, E; Simões, A. , 2004 , Inteligência Artificial. Fundamentos e Aplicações , Editora de Informática
Alpaydin, E. , 2020 , Introduction to machine learning , MIT press
Fagin, R.; Halpern, J. Y.; Moses, Y. & Vardi, M. , 1995 , Reasoning about Knowledge , Mit Press
Nilsson, N. J. , 1998 , Artificial Intelligence: A New Synthesis , Morgan Kaufmann Publishers
Poole, D.; Mackworth, A. & Goebel, R. , 1998 , Computational Intelligence. A logical approach , Oxford University Press
Russel, S. & Norvig, P. , 2020 , Artificial Intelligence: A Modern Approach, 4th ed. , Pearson
Resnik, M. , 1987 , Choices: An introduction to Decision Theory , University of Minnesota Press
Rich, E & Knight, K. , 1991 , Artificial Intelligence - 2nd ed. , McGraw-Hill Education

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
Lectures, Problem Solving, discussion sessions. Laboratorial practices . Use of audiovisual media for lecturing . Using Robots as Intelligent Agents Exam: The aims to promote reading of the topics lected. The exam consists of 5 conceptual questions a (values with 4 points each) on topics lected in lectures. 50% of Final Grade. Project: This involves, build and program a robot to solve a given problem using Lego ® "Robotic Invention System Mindstroms". In order to approve it is mandatory that the robot can solve the proposed problem. The items that define the final grade are: Quality of Programming. Quality of the Report. Quality of the Robot. 50% of the final grade.