Artificial Intelligence

Objectives:

Reasoning, learning, searching for new information, extracting models from knowledge base systems and adapting to unpredictable situations are key fac- tors in any modern computer system. The goal of this course is to investigate knowledge representation models and algorithms that are useful to reason about facts and situations and are suitable to support advanced search and optimisation strategies other than learning systems. In fact, learning from ex- perience and from errors is a crucial aspect for any intelligent system that has to interact with an external environment. The course moves from simple to complex problems introducing concept such as heuristic search and approxi- mation algorithms. These are important tools to allow the student to theoreti- cally analyze and practically solve real life situations.

Contents:

The course covers some of the most important topics in Artificial Intelligence. In particular we start by investigating blind search strategies such as breadth and depth search and we move to heuristic algorithms like greedy and A*. Next, two players’ game strategies like min-max and alpha-beta are investigated. To solve complex problems the student is introduced to meta-heuristic algorithms such as simulated annealing, genetic algorithms and ant colony optimisation. We also in- vestigate how to extract knowledge and models from data base with the main data mining tools and algorithms. During the course students compete to solve a complex problem and the Artificial Intelligence CUP is assigned to the best student.