Curricular Unit:			Code:
Artificial Intelligence			906IART
Year:	Level:	Course:	Credits:
1	Master	Computer Systems Engineering (Information Systems and Multimedia)	6 ects
Learning Period:		Language of Instruction:	Total Hours:
		Portuguese/English	78
Learning Outcomes of the Curricular Unit:
Upon successful completion of this course unit students should be able to (learning outcomes - LO): LO1-Understand the notion of intelligent agents LO2-formulate problem solving using search agents LO3-apply uninformed, informed search and local search methods LO4-apply evolutionary algorithms LO5-apply search methods in adversarial environments LO6-Experiment Logic Agents and Logic Programming LO7-apply basic learning models: statistics and decision tree construction LO8-evaluate models learned through observations
Syllabus:
1. Introduction to Artificial Intelligence (AI) 2. Intelligent Agents (IAG) 2.1 IAG Project 2.2 Environments and Properties 3. Methods of Problem Solving (PS) 3.1 PS through Search Agents 3.1.1 Problem formulation, examples and solutions 3.1.2 Uninformed search: analysis and comparison 3.2 Informed search 3.3 Evolutionary Computing 3.4 Constraint Satisfaction Problems (CSP) 3.5 Adversarial Search (Games) 4. Knowledge Representation, Reasoning and Logic 4.1 Introduction, Structures and Objects 4.3 Handling of Symbolic Structures 4.4 Knowledge Based Agent 4.5 Representation, Reasoning and Logic 4.6 Transformation of Knowledge into Action 4.7 Propositional, Predicate, Modal and Temporal Logic 5. Introduction to Logic Programming 5.1 Clauses, Facts and Rules 5.2 Syntax and Data Types in Prolog 5.3 Unification and Backtracking Mechanisms 6. Machine Learning 6.1 Inductive learning 6.2 Neural Networks
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
The syllabus presented are consistent with the learning objectives of the curricular unit since there is a large convergence between the table of contents and the knowledge that the student is supposed to acquire in each of the program topics. The fundamental concepts of artificial intelligence and intelligent agents are presented in the introductory chapters, in the following chapters are presented various methods and techniques of artificial intelligence such as the search and logic agents. It is also presented the area of machine learning. The learning objectives are achieved by supplementing the theoretical concepts with concrete examples and exercises run in lab environment using appropriate software
Teaching Methodologies (Including Evaluation):
Theo-pra (TP) and pra-lab (PL) components. In TP classes, concepts are presented interspersed with examples and exercises. In PL classes are posed and solved exercises and problems, eventually using software. Final_Grade_IA = (1/2) x Grade_TP + (1/2) x Grade_PL Required: Grade_TP> = 10 Grade_PL> = 10 Grade_PL: compulsory for continuous evaluation and is not subject to examination. It consists of a programming project carried out during the semester. If student does not submit the project will have zero and can only carry out the project in a subsequent edition of the UC. Grade_PL = Practical project grade Grade_TP: In continuous evaluation, two frequencies F1 and F2, Grade_TP = (50%) x F1 + (50%) x F2 On exam, Grade_TP = Grade_TP_Examination If only TP or PL is positive, it is valid for a maximum period of two academic years after the year in which the student has obtained approval in this component. During this period the UC is not completed (NC)
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
The teaching/learning methodology applied in this curricular unit as well as its evaluation system is perfectly aligned with the objectives to be attained by the students at the end of the term. The theoretical concepts are presented, discussed, applied and evaluated in the context of lectures, which guarantees students a solid foundation to understand the challenges facing this area of knowledge. On the other hand, so that the study is not restricted to conceptual models, in the practical lessons are presented concrete case studies and implemented solutions for real problems using appropriate software. This combination guarantees training for students that allows them to meet the scientific goals, essential to a good understanding of the theme, as well as the ability to adapt to technological changes. The evaluation process consists of theoretical tests and practical work also guarantees a correct balance between the efforts dedicated to both components. The objective is to train professionals’ specialized in state-of-the-art techniques and tools but also ensure its ability to follow future developments. In this curriculum unit the problem and concepts of the artificial intelligence are presented and evaluated in theoretical component. These concepts are then applied in the resolution of worksheets and practical work in the context of practical classes
Reading:
[1] Russell, Stuart ; Norvig, Peter, “Artificial Intelligence: A Modern Approach”, Prentice Hall, 3rd edition, 2009 [2] Costa, E.; Simões, A., “Inteligência Artificial - Fundamentos e Aplicações”, Editora FCA, 2ª edição, 2008 [3] Witten, Frank, Hall, Pal, “Data Mining: Practical Machine Learning Tools and Techniques, 4rd Edition”, Morgan Kaufmann, 2017 [4] I. Bratko, "Prolog Programming for Artificial Intelligence, 4th edition", Pearson Education, 2011