92858 - Autonomous and Adaptive Systems M

Course Unit Page

  • Teacher Mirco Musolesi

  • Credits 8

  • SSD ING-INF/05

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Computer Engineering (cod. 0937)

  • Teaching resources on Virtuale

Academic Year 2019/2020

Learning outcomes

The goal of this module is to provide a solid introduction to the design of autonomous and adaptive computing systems from a theoretical and practical point of view. Topics will include principles of autonomous system design, reinforcement learning, game-theoretic approaches to cooperation and coordination, bio-inspired systems, complex adaptive systems, and computational social systems. The module will also cover several practical applications from a variety of fields including but not limited to distributed and networked systems, mobile and ubiquitous systems, robotic systems, and vehicular and transportation systems.

Course contents

Introduction to the design of autonomous systems and intelligent machines.

Review of Machine Learning/Deep Learning fundamentals.

Introduction to Reinforcement Learning (RL).

Applications of RL to games.

Applications of RL to the design of distributed, networked and mobile systems.

Applications of RL to classic control theory problems and robotics.

RL and cognitive sciences&neuroscience.

Intelligent machines and creativity: Generative Deep Learning, AI and the Arts.

Introduction to algorithmic game theory for autonomous systems: cooperation and coordination, Multi-Agent Reinforcement Learning.

Bio-inspired adaptive systems.

Autonomous and mobile robots.

Driverless cars and autonomous transportation systems.

Ethical implications of AI and autonomous systems.

Machine intelligence, super-intelligence, self-awareness, controllability and the future.


During the course, the instructor will provide an extensive list of pointers (scientific papers, technical documentation, books, etc.) for each topic.

Useful textbooks for the course include the following:

  • Francois Chollet. Deep Learning with Python. Manning. 2017.
  • Dario Floreano and Claudio Mattiussi. Bio-inspired Artificial Intelligence: Theories, Methods and Technologies. MIT Press. 2008.
  • David Foster. Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play. O’Reilly. 2019.
  • Robin R. Murphy. Introduction to AI Robotics. Second Edition. MIT Press. 2019.
  • Peter Norvig and Stuart J. Russell. Artificial Intelligence: A Modern Approach. MIT Press. 2010.
  • Max Pumperla and Kevin Ferguson. Deep Learning and the Game of Go. Manning. 2019.
  • Roland Siegwart, Illah R. Nourbakhsh, Davide Scaramuzza, Ronald C. Arkin. introduction to Autonomous Mobile Robots. Second Edition. MIT Press. 2011.
  • Richard R. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press 2018.

The teaching material (slides, etc.) can be found here.

Teaching methods

Lectures in the classroom and in the lab.

Assessment methods

Oral exam with discussion of a project (90%) and class participation (10%).

Office hours

See the website of Mirco Musolesi