This course provides a comprehensive view of Artificial Intelligence, by selecting for study some of the main topics of the discipline: search, knowledge representation and resoning, planning and learning. The several approaches and paradigms are illustrated in the several course projects.

These subjects are treated in sufficient detail that allows students to solve toy problems as well as understand the difficulties of real problem instances. In the lab classes students will develop selected programming projects where the learnt techniques can be applied to solve some problems, both by implementing small prototypes and by modeling the problems in existing tools.

In this way, it is expected to get the interest of students to these subjects and to more advanced techniques, to be explored in later Master courses.

• Paradigms, areas and applications of Artificial Intelligence.
• Blind, informed and local search algorithms. Heuristics and evaluation functions. Complexity.
• Inference in propositional logic, first order logic and Bayesian networks. Limitations.
• Planninglanguages and algorithms for plan generation.
• Learning approaches and its essential algorithms.

• Model search problems, specify heurisitics and assess its behaviour in practice.
• Implement search algorithms. Parameterise and experiment with the algorithms.
• Model and solve problems withLogic Programming.
• Model knowledge with Bayesian networks. Perform predictive and diagnosis tasks.

• Choose appropriately instruments weighing thesolution quality against the time/space necessary to obtain it.
• Justify or choose solutions.
• Do team work with report writing and deadline fulfillment.
• Abstract modelling capabilities.

Theory:

1.Overview of Artificial Intelligence and its applications

2.Problem-­solving

2.1.Search agents and search problems.

2.2.Blind search.

2.3.Heuristic search in state-­space graphs. A* algorithm.

2.4.Local search and optimisation problems.

2.5.Adaptative agents.

2.6.Constraint Satisfaction Problems.

2.7.Adversarial search.

3.Knowledge Representation and Reasoning

3.1.Propositional logic.

3.2.First-­order logic.

3.3.Logic programming

4.Planning

4.1.Logical representation of classical planning problems.

4.2.Planning languages and algorithms. Partial order planning.

4.3.GRAPHPLAN

5.Uncertainty and probabilistic reasoning

5.1.Probability theory and Bayes'''''''''''''''''''''''''''''''' rule.

5.2.Bayesian networks.

6.Machine Learning

6.1.Learning agents. Approaches to the learning problem.

6.2.Conceptual and inductive learning.

6.3.Neuronal networks.

7.Conclusion

Labs:

Informed Search
Local Search
Adverserial Search
Logic Programming
Planning
Uncertainty and Proabilistic Reasoning
Automated Learning

A good knowledge of the Java language is asssumed as well as hands-on experience with Eclipse framework.