Artificial intelligence is set to revolutionise the way that we work, live and interact.<br>This course is a study of the basic building blocks of decision-making agents, which are abstract entities living in an uncertain environment and are guided towards the realisation of given objectives. On top of this, the environment is usually inhabited by other agents, which may or may not strive to achieve similar objectives. The task is to take the best possible decision that can be taken given the (incomplete) information available.<br>These simple models are the basis of a number of important achievements in AI, and combine the use of logical, game-theoretic and algorithmic analysis.<br>The course will be an exploration of the basic methodologies for the design of artificial agents in complex environments. The course will first start with classical AI approaches where these agents are goal-oriented and take decisions in a potentially unknown environment.<br>Then it will move on to more sophisticated models allowing agents to have a representation of the other agents, their potential decisions and their goal, a representation about the representations of other agents, and so forth. This induces complex patterns of strategic reasoning, both in competitive and cooperative interactions, which need to be formally modelled and analysed.<br>These agent-based systems are built upon three important methodologies: Logic, because of the focus on reasoning, Game-Theory, because of the focus on strategies, and Algorithms, because of the focus on artificial agents.<br>You will learn the basics of how to program in python and apply this knowledge to studying, and building your own, learning algorithms.