Class Artificial Intelligence

This module is an introduction to the basic concepts and techniques of Artificial Intelligence, with three main focus areas. First, the formalisation of what a machine is, both in terms of the symbol manipulation of the Turing machine, and in the McCulloch and Pitts machines that work with interconnection patterns between nodes in neural networks. Second, the concept of rational agent in AI, that emerges the intersection with the Cognitive Sciences, and the various implementations of comprehensive structured search algorithms (informed and not informed). Still within this focus area, the concept of stochastic search and Constraint Satisfaction Problems (CSPs) are also introduced. Finally, in the third area of focus, students learn some notions and uses of some of the more advanced artificial intelligence algorithms that are used today.

1. Basic Concepts
   1. Definitions of AI
   2. Turing Machine
   3. McCulloch and Pitts Neural Networks
   4. How to Analyze Machines? State Transition Diagrams
2. Search
   1. The Concept of a Search Agent in AI
   2. Spaces and Search Graphs
   3. Uninformed Search: British Museum, DFS, BFS
   4. Informed Search: Dijkstra and A*
   5. Adversarial Search
3. Constraint Satisfaction Problems
4. Basic Notions of Recommendation Systems
5. The Future of AI
   1. Critical Analysis of Recent Articles in AI

To be described

• Russell, S., & Norvig, P. (2020). Artificial intelligence: a modern approach. 4th edition. http://aima.cs.berkeley.edu