Students who successfully complete this course will be able to:

1. Understand what is artificial intelligence, its origins, evolution and application areas.
2. Represent and solve problems based on the concept of agent, using reactive and deliberative architectures.
3. Understand the notions of internal representation, deliberation and reasoning in the context of an agent architecture.
4. Implement automated reasoning mechanisms based on state space search methods and characterize these methods in terms of computational complexity.
5. Implement automated reasoning mechanisms based on Markov decision processes and characterize these methods in terms of computational complexity.
6. Understand the concepts of adaptation and learning in the context of an agent architecture.
7. Understand the concept of interactive learning and implement this concept in the form of reinforcement learning mechanisms.
8. Represent and solve problems based on reinforcement learning and characterize reinforcement learning in the context of Markov decision processes.

Curricular Unit Form