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Master in Artificial Intelligence and Robotics
Learning in Autonomous Systems
Proff. Luca Iocchi, Giorgio Grisetti
A.A. 2015/2016
NOTE. Since A.Y. 2016/17 this course has been replaced by Probabilistic Robotics (given by Prof. Grisetti) and a section of Artificial Intelligence (given by Prof. Iocchi).
Teachers
Prof. Luca Iocchi (Home page)
Prof. Giorgio Grisetti (Home page)
Dipartimento di Ingegneria informatica automatica e gestionale “Antonio Ruberti”
Università di Roma “La Sapienza”
Via Ariosto 25, Roma 00185, Italy.
Room B115
E-mail: iocchi/grisetti@dis.uniroma1.it
In e-mail messages, please use the prefix "[LAS]" to the subject.
Visiting Professor
Prof. Marc Hanheide (Home page)
School of Computer Science
University of Lincoln, UK
Office: DIAG, Room B114
Schedule
Monday 14:00 - 17:15 - Room B2
Friday 10:15 - 11:45 - Room B2
Last lecture will be on May 16th.
Office hours
Prof. Luca Iocchi - Friday 12:00 (during the lectures) or appointment by e-mail
Prof. Giorgio Grisetti - appointment by e-mail
Description of the course
The course gives 6 CFU and can be attended by any student enrolled in the Master degrees in Artificial Intelligence and Robotics, Computer Science and Control Engineering.
Objectives
The goal of the course is to present techniques and tools for machine learning in complex dynamic systems and autonomous agents. In particular, the course will describe probabilistic models for representing dynamic systems and autonomous agents, reinforcement learning techniques, learning in graphical models, state estimation techniques. The course will also present many examples of successful application of Machine Learning algorithms in different application scenarios.
At the end of the course the student will be able to use the addressed techniques and tools in modeling and solving learning problems for complex dynamic systems. Students will gain the capability of solving complex learning problems with dynamic systems, by devising a proper formulation of the problem, performing adequate design and implementation choices, designing and executing effective experiments to evaluate the results obtained.
Syllabus
Introduction
Typical Problems for robotic applications
Basics of probabilities and linear algebra
Models of dynamic systems
General concepts
Model taxonomy
Markov Decision Processes
Hidden Markov Models (forward, backward)
Dynamic Bayesian Networks
Partially Observable Markov Decision Processes
Probabilistic Graphical Models
Reinforcement Learning
Q-Learning algorithm
Non-deterministic algorithms
Inverse Reinforcement Learning
RL in plan space
Bayes Filtering in DBN
Discrete filters (forward)
Particle filters
Learning in Probabilistic Graphical Models
Learning in HMM (Baum-Welch)
Learning in DBN: estimating CPD from supervised data sets
Multi-Agent Learning
Multi-source multi-object tracking
Multi-agent learning
Teaching Material
Provided in the Lectures section
Other Suggested textbooks
They are or will be available in the DIAG library.
Probabilistic Graphical Models
Daphne Koller and Nir Friedman
MIT Press, 2009.
Sebastian Thrun, Wolfram Burgard Dieter Fox
MIT Press.
Tom Mitchell
McGraw Hill, 1997.