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B028333 - MULTIAGENT SYSTEMS
Main information
Teaching Language
Course Content
Learning Objectives
Course program
Academic Year 2019-20
Course year
Second year - First Semester
Belonging Department
Information Engineering (DINFO)
Modulo di sola Frequenza of
Scientific Area
ING-INF/04 - SYSTEMS AND CONTROL ENGINEERING
Credits
6
Teaching Hours
48
Teaching Term
23/09/2019 ⇒ 20/12/2019
Attendance required
No
Type of Evaluation
Giudizio Finale
Course Content
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Course program
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Lectureship
Mutuality
Course teached as:
B028335 - SISTEMI MULTIAGENTE
Second Cycle Degree in ELECTRICAL AND AUTOMATION ENGINEERING
Curriculum INGEGNERIA ELETTRICA
B028335 - SISTEMI MULTIAGENTE
Second Cycle Degree in ELECTRICAL AND AUTOMATION ENGINEERING
Curriculum INGEGNERIA ELETTRICA
Teaching Language
Italian
Course Content
The covered topics include: multi-agent system models, distributed control and data processing algorithms, reinforcement learning, applications (sensor networks, multi-robot systems, formation control, distributed data processing and machine learning, etc. ).
Learning Objectives
The course aims to provide methodologies for modeling, analyzing, and designing intelligent multi-agent systems able to carry out complex tasks, taking into account:
both physical and virtual agents (autonomous vehicles, robots, sensors, processing units, pieces of software);
a high number of interconnected, possibly heterogeneous, agents;
decentralized architectures.
both physical and virtual agents (autonomous vehicles, robots, sensors, processing units, pieces of software);
a high number of interconnected, possibly heterogeneous, agents;
decentralized architectures.
Course program
1. INTRODUCTION TO MULTI-AGENT SYSTEMS (MASs)
What is a multi-agent system? Cooperative vs competitive multi-agent systems. Examples of multi-agent systems in science and engineering. Motivating problems.
2. ELEMENTS OF GRAPH THEORY
Connectivity of a graph. The Laplacian of a graph: properties and applications (graph partinioning and spectral clustering).
3. SYNCHRONIZATION AND COORDINATION IN MULTI-AGENT SYSTEMS
Consensus for undirected and directed graphs. Applications (social networks, distibuted computing). Lyapunov functions and gradient systems. Synchronization and Coordination via Lyapunov functions.
4. MULTI-ROBOT SYSTEMS
Mobile robot models. Coordination algorithms for rendez-vous, formation control, and flocking problems. Connectivity maintenance and collision avoidance. Covering and exploration.
5. MULTI-AGENT OPTIMIZATION AND LEARNING
Elements of optimization and machine learning. Distributed optimization over networks. Distributed linear least squares. Distributed regression over networks.
6. MULTI-AGENT INFORMATION FUSION
Elements of Bayesian estimation and information fusion. Sensor networks and distributed estimation. Distributed Kalman filtering. Applications.
7. REINFORCEMENT LEARNING
Stochastic dynamic programming. Approximate dynamic programming: value and policy iteration. Reinforcement learning: time-difference and Q-learning. Exploration vs exploitation. Multi-agent reinforcement learning.
What is a multi-agent system? Cooperative vs competitive multi-agent systems. Examples of multi-agent systems in science and engineering. Motivating problems.
2. ELEMENTS OF GRAPH THEORY
Connectivity of a graph. The Laplacian of a graph: properties and applications (graph partinioning and spectral clustering).
3. SYNCHRONIZATION AND COORDINATION IN MULTI-AGENT SYSTEMS
Consensus for undirected and directed graphs. Applications (social networks, distibuted computing). Lyapunov functions and gradient systems. Synchronization and Coordination via Lyapunov functions.
4. MULTI-ROBOT SYSTEMS
Mobile robot models. Coordination algorithms for rendez-vous, formation control, and flocking problems. Connectivity maintenance and collision avoidance. Covering and exploration.
5. MULTI-AGENT OPTIMIZATION AND LEARNING
Elements of optimization and machine learning. Distributed optimization over networks. Distributed linear least squares. Distributed regression over networks.
6. MULTI-AGENT INFORMATION FUSION
Elements of Bayesian estimation and information fusion. Sensor networks and distributed estimation. Distributed Kalman filtering. Applications.
7. REINFORCEMENT LEARNING
Stochastic dynamic programming. Approximate dynamic programming: value and policy iteration. Reinforcement learning: time-difference and Q-learning. Exploration vs exploitation. Multi-agent reinforcement learning.