|Responsible unit:||Department of Informatics Engineering|
|Course/CS Responsible:||Master in Informatics and Computing Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|MIEIC||168||Syllabus since 2009/2010||3||-||6||56||162|
|Ana Paula Cunha da Rocha|
|Luís Paulo Gonçalves dos Reis|
This course provides a set of subjects (topics) that are the core of the Artificial Intelligence and Intelligent System area.
The main objectives are:
Percentual Distribution: Scientific component: 60%; Technological component: 40%
At the end of the course, students should be able to represent, acquire, manipulate and apply knowledge using computer systems. More specifically, the student should be able to:
Knowledge of algorithms, data structures, and programming.
Definition of AI. Fundamentals, Scope, Evolution and Chronology of AI. Problems and Approaches of AI and Intelligent Systems. AI Applications.
The Concept of Agent. Environments. Agent Architectures: Reactive, Deliberative, Goal-Based, Utility-Based, Learning and BDI. Multi-Agent Systems: Concept, Motivation, Architectures, Communication, Coordination. Practical Examples of Application.
III. Problem Solving Methods
Problem Formulation. State Space. Search Strategy. Uninformed Search: Breadth First, Depth First, Uniform Cost, Iterative Deepening, Bidirectional Research. Intelligent Search: Greedy Search, A* Algorithm. Search with Adversaries: Game Search, Minimax Algorithm, Alpha-Beta Cuts, Search with Imperfect Information. Practical Examples of Application.
Formulation of Decision/Optimization Problems. Hill-Climbing Algorithm, Simulated Annealing, Tabu Search, "Ant Colony". Genetic Algorithms and Evolutionary Computation. Constraint Satisfaction. Practical Examples of Application.
Knowledge Representation and Reasoning. Propositional and Predicate Logic. Semantic Networks, Frames, Rules, and Ontologies. Logic Programming and Programming with Constraints. Reasoning with Uncertain Knowledge. Knowledge-Based Systems. Expert Systems: Inference Engine, Generation of Explanations, Generic Systems ("Shells"). Practical Examples of Application.
Types of Learning. Learning concepts. Learning for example, by analogy, based on explanations. Inductive Learning: Algorithms ID3 and C4.5. Artificial Neural Networks: Basic principles and fundamental algorithms. Support Vector Machines. Reinforcement Learning. Deep Learning. Practical Application Examples.
VII. Natural Language Processing
Processing Levels. Syntactic and Semantic Analysis. ATN, Semantic Grammars and Case Grammars. Classical Approach and Use of Logic. Grammar with Definite Clauses. Extraposition Grammar. Statistical Approach. Text Mining. Practical Application Examples.
VIII. Advanced Topics in Artificial Intelligence
Perception/Vision, Communication, Interaction, Planning, Scheduling, Robotics, Intelligent Simulation, Social Intelligence. Applications of Artificial Intelligence and Intelligent Systems. The Future of AI. IA and the Society. Beneficial IA. Machine Ethics. Weak and Strong IA. Super Intelligence. The Singularity.
Theoretical classes: exposition with interaction. Theoretical-practical classes: modeling, problem-solving, programming exercises and project development.
|Frequência das aulas||56,00|
Not exceed the absence limit allowed and have a minimum of 37,5% in the distributed classification (DC)
Distributed Classification (DC): weight=50%, including two practical works (% related to the distributed classification):
Test/Exam (EC): weight=50% (2h30m test with consultation).
To pass, the student must have a minimum of 37.5% in each of the two evaluation components, distributed (DC) and exam (EC).
Two practical assignment, respective reports, and demos (weight=50%) and an exam (weight=50%).
To get approved, the student must have a grade equal or higher to 37,5% in each of the evaluation items.
Two practical assignment and the respective reports, and demos (weight=50%) and an exam (weight=50%).
To be approved, the student must have a grade equal or higher to 37,5% in each of the evaluation items.
Assignment, exam or both.
Mini-exams' grades are not taken into account for this purpose. Its valuation will be added to the Exam component.