The course gives 12 credits and is structured in the following four sections (please refer to each of the sections and to the home page of the teachers for additional details, including exams).

Exams

Each section has specific dates (check each section web-site). Book through Infostud only when you have passed all the sections (the professor marked (*) is in charge of the registration).

Section: Automated Reasoning
Prof. Paolo Liberatore
3 Credits
I Semester

Programme:
Introduction to the propositional calculus; the Davis-Putnam procedure; tableau method for propositional logic; first-order logic; tableau methods for first-order logic; natural deduction; sequent calculus; Hilbert system; resolution; modal logics; tableau for modal logics; linear temporal logics; computational tree logic; the labeling algoritm for linear and computational tree logics.

Section: Robot Programming
Prof. Daniele Nardi
3 Credits
I Semester
Classes start Tuesday September 27th, Tuesdays 14:00-15:30, Room A3

Classes are moved since October 4th, Tuesdays 8:30-11:45, Room A3

Programme:
Robot programming requires a deep knowledge of the programming techniques and the programming language chosen for software development. In addition, the software for robotic applications is often built by means of specialized development tools. The goal of the course is to discuss a case study, in order to provide a systematic approach to robot programming. Specifically, robot programming is addressed using C++ as basic programming language, ROS and OpenRDK as development frameworks. The the target robotic platforms are wheeled robots and the NAO humanoid robot. Topics:
1. Robot programming in ROS and Open-RDK
2. Robotic Platforms and simulation environments
3. Case studies in Perception, Navigation, Mapping, Localization, Action planning, Plan execution, Human Robot Interaction, Multi Robots.

Section: Introduction to Pattern Recognition
Prof. Fiora Pirri
3 Credits
II Semester

Programme:
Pattern Recognition is branch of artificial intelligence concerned with the classification or description of observations. Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. Topics:
1. Introduction: Basic concepts & Components of a PR system.
2. Features Extraction Methods.
3. Representation and design of Classifiers.
4. Bayesian, Discriminative and Generative approaches.
5. Applications: sound, face, ATR, fingerprints, handwritten recognition.

Suggested readings: 
     1. Richard O. Duda, Peter E. Hart and David G. Stork : Pattern Classification and Scene Analysis. J. Wiley & Sons, New York, 2nd edition 2000. Exercise Book
     2. Christopher M. Bishop, Pattern Recognition and Machine Learning Series: Information Science and Statistics  2006, XX, 740 p. 304

Section: Artificial Intelligence in Games and Videogames
Prof. Marco Schaerf
3 Credits
II Semester

Programme:
In this course we will survey the Artificial Intelligence (AI) techniques used in games (chess, poker, ...) and videogames. Games have been from the beginning an important field of application for AI, most remarkable is the development of chess programs, more recently the focus has also included poker and viedogames. In modern videogames a must for any new title is its ability to have "realistic" characters, that behave as "intelligently" as possible. This requires a sophisticated use of AI techniques. The course will survey a wide number of AI techniques and the student is supposed to choose a topic of interest and study it in detail and present the results in a seminar.