Multi-Agent systems: the new AI solution to complex problems
Problem Statement:
Multi-agent systems consist of multiple interacting intelligent agents that can work collaboratively to solve tasks that are beyond the capabilities of individual agents. The study will explore the design, coordination, and optimization of these systems to enhance their effectiveness in various applications such as
- Autonomous Vehicles: Multi-agent systems are used to coordinate fleets of autonomous vehicles, ensuring efficient traffic flow and reducing congestion. Each vehicle acts as an agent that communicates with others to optimize routes and avoid collisions
- Smart Grids: In energy management, multi-agent systems help balance supply and demand in smart grids. Agents can represent different energy sources and consumers, working together to optimize energy distribution and reduce waste
Tasks:
- Literature Review: Conduct a comprehensive review of existing architecture and tools on multi-agent systems.
- Define Architecture: Develop the architecture for multi-agent systems, detailing the communication protocols and coordination mechanisms.
- Implementation: Implement the proposed architecture and algorithms in a simulated environment to test their functionality.
- Performance Analysis: Evaluate the performance of multi-agent systems in various scenarios, focusing on efficiency, scalability, and robustness.
- Case Studies: Identify and analyze a real-world application of multi-agent systems(free to choose the sector, such as healthcare, transportation, and finance).
- EXTRA Algorithm Development: Create algorithms that enable effective collaboration and task distribution among agents in multi-agent systems.
Work:
10% Theory, 70% Simulations, 20%Writing
Contact:
Alessandro Chiumento (a.chiumento@utwente.nl)