Base Knowledge
Basic AI concepts (agents)
Basics of procedural programming
Teaching Methodologies
– 2 theoretical hours each week used to present new concepts and topics in the field of artificial intelligence focused on the representation of knowledge and reasoning using different intelligent algorithms. Presentation of subjects using slides and practical examples.
– 2 practical hours each week in which students have the opportunity to apply the concepts learned to solve concrete problems, using different tools and several practical cases.
Learning Results
Goals:
It is intended that students acquire knowledge in the area of intelligent systems, namely in the various forms of knowledge representation and reasoning methodologies, based on different algorithms in the area of artificial intelligence, namely rule-based systems, case-based systems, fuzzy systems, Bayesian networks, neural networks and clustering algorithms.
Skills
Knowledge and understanding:
• Explain the main forms of knowledge representation.
• Identify and explain the main types of intelligent reasoning
• Understand the main characteristics of different forms of knowledge and reasoning and forms of machine learning and be able to make accurate and well-founded choices to solve problems.
Application of knowledge
• Acquisition and understanding of concepts essential to the representation of knowledge in intelligent systems.
• Acquisition and application of knowledge about understanding various forms of reasoning and forms of machine learning.
• Ability to design, implement, and evaluate intelligent applications using the various knowledge and reasoning methodologies discussed.
Communication
• Prepare clear documentation as part of the development of practical work, identifying and justifying the main decisions made.
Autonomy and self-learning
• Ability to carry out autonomous and group work.
• Development and autonomy in learning.
• Increase the ability to propose intelligent solutions to solve problems with different characteristics.
Program
1) Forms of knowledge representation
2) Rule-based reasoning:
Introduction to Expert Systems
Generalities and Basic Principles
Rules
Inference Engine: Forward and Backward Chaining
3) Case-based reasoning
The RBC paradigm
Aamodt & Plaza Diagram
Case Representation and Memory Models
Similarity Functions
4) Fuzzy Reasoning
Introduction to fuzzy logic
Fuzzy sets and numbers
Computation with linguistic terms
Mamdani Inference
5) Probabilistic reasoning
Bayesian Networks
Bayes theorem
Joint and Conditioned Probability
Construction of Bayesian Networks
Application examples
6) Reasoning based on machine learning
Linear regression
Neural Networks
– Perceptron
– Gradient Descent and delta rule
– Multilayer networks
– BackPropagation Algorithm
– Other neural networks
Clustering Algorithms
– Density
– Distribution
– Centroid
– Hierarchical
– Comparison of various algorithms: K-means, DBSCAN, BIRCH, etc.
Curricular Unit Teachers
Anabela Borges SimõesInternship(s)
NAO
Bibliography
Bacchus, F. (1990). Representing and reasoning with probabilistic knowledge : a logical approach to probabilities. The MIT Press, ISBN 0-262-02317-2
Cota biblioteca do ISEC: 1A-4-23 (ISEC) – 05769
Jackson, P., (1998). Introduction to Expert Systems (3ª ed.). Boston: Addison-Wesley.
Watson, I., (1997). Applying Case Based Reasoning (1ª ed.). Burlington: Morgan Kaufmann.
Zimmerman, J. (2001). Fuzzy Set Theory and Its Applications (4ª ed.). Heidelberg: Springer
Lenz, M. (1998). Case-based reasoning technology : from foundations to applications, Springer,ISBN 3-540-64572-1
Cota biblioteca do ISEC: 1A-4-104 (ISEC) – 10529
Nguyen , H. T. (1995). Theoretical aspects of fuzzy control. John Wiley, 1995, ISBN 0-471-02079-6
Cota biblioteca do ISEC: 1A-4-56 (ISEC) – 06715
Neapolitan , R. (2004). Learning Bayesian networks, Pearson/Prentice Hall, ISBN 0-13-012534-2
Cota biblioteca do ISEC: 1A-4-166 (ISEC) – 15034
Hosmer, D., Lemeshow, S., May, S. (2008). Applied survival analysis : regression modeling of time-to-event data, Wiley Interscience, ISBN 978-0-471-75499-2
Cota biblioteca do ISEC: 3-3-175 (ISEC) – 14526
Fausett, L. (1994). Fundamentals of neural networks : Architectures, algorithms, and applications, Prentice Hall International, ISBN/ISSN: ISBN 0-13-042250-9
Cota biblioteca do ISEC: 1A-4-52 (ISEC) – 07087
Watt, J. et al (2020). Machine learning refined : foundations, algorithms and applications, Cambridge University Press, ISBN 978-1-108-48072-7
Cota biblioteca do ISEC: 1A-4-200 (ISEC) – 19044