Curricular Unit:Code:
Artificial Intelligence834IART
Year:Level:Course:Credits:
1MasterComputer Systems Engineering (Mobile Computation)6 ects
Learning Period:Language of Instruction:Total Hours:
Spring SemesterPortuguese/English78
Learning Outcomes of the Curricular Unit:
Competence levels (Dublin descriptors):1. Knowledge and understanding
1.1. Evolution of AI and its impact in several areas of society.
1.2. Different approaches / areas of AI.
1.3. Basic AI algorithms.
1.4. From the skills required to develop IA systems in general.
2. Applying knowledge and understanding2.1. Integrate systems development teams / applications that use IA.
2.2. Use and integrate AI algorithms consistently with the goals to be achieved.
3. Making judgements:3.1. Choice of AI approaches appropriate to specific situations
3.2. Integrate and participate in the decision making of IA project development teams.
4. Communication:
4.1. Describe AI-based systems.
4.2. Work in a group.
4.3. Argue orally and in writing.
5. Autonomy and learning skills:5.1. Autonomous learning of new technologies in development
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):
Theoretical-practical (TP) and practical-laboratory (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