Course teached as: B031274 - AUTONOMOUS AGENTS AND INTELLIGENT ROBOTICS Second Cycle Degree in ARTIFICIAL INTELLIGENCE
Teaching Language
ITALIAN
Course Content
The covered topics include: intelligent agents and multi-agent system models, distributed control and data processing algorithms, reinforcement learning, applications (robot navigation, sensor networks, multi-robot systems, formation control, distributed data processing and machine learning, etc. ).
The course aims to provide methodologies for modeling, analyzing, and designing intelligent agents able to carry out complex tasks, taking into account:
both physical and virtual agents (autonomous vehicles, robots, sensors, processing units, pieces of software); interconnected, possibly heterogeneous, agents;
decentralized architectures.
Course program
1. INTELLIGENT AGENTS
Agents and multi-agent systems. Cooperative vs competitive multi-agent systems. Paradigms of intelligent agents. Planning vs behaviors.
2. ROBOT MOTION PLANNING
Configuration space and motion planning. Planning-based approaches: geometry-based, cell decomposition, PRM and RRT algorithms. Behavior-based approaches: Bug algorithms, artificial potential fields.
3. ELEMENTS OF GRAPH THEORY
Connectivity of a graph. The Laplacian of a graph: properties and applications (graph partinioning and spectral clustering).
4. SYNCHRONIZATION AND COORDINATION IN MULTI-AGENT SYSTEMS
Consensus for undirected and directed graphs. Applications (social networks, disrtibuted computing). Artificial potential fields for synchronization and coordination.
5. MULTI-ROBOT SYSTEMS
Coordination algorithms for rendez-vous, formation control, and flocking problems. Connectivity maintenance and collision avoidance. Covering. Mapping and exploration.
6. MULTI-AGENT OPTIMIZATION AND LEARNING
Distributed regression over networks. Distributed linear least squares. Distributed optimization over networks. Centralized and distributed information fusion. Application to sensor networks and distributed estimation.
7. REINFORCEMENT LEARNING
Markov Decision Processes. Stochastic dynamic programming. Value and policy iteration. Reinforcement learning: time-difference, Monte-Carlo method, Q-learning, policy gradient. Exploration vs exploitation. Multi-agent reinforcement learning.