Graduate seminar on Distributed and Decentralized Task Allocation in Flexible Swarms by Yigal Koifman, supervised by Prof. Alfred M. Bruckstein and Dr. Ariel Barel.
Multi-agent systems provide a powerful platform for solving complex tasks in dynamic environments, yet achieving coordination without centralized control or direct communication remains a core challenge. This seminar will present a unified, scalable, and robust framework for distributed task allocation that emerges solely from local interactions in both homogeneous and heterogeneous swarms.
We introduce and analyze decentralized control methods based on geometric interaction rules, a flexible swarm model that preserves cohesion and enables adaptive motion, and learning-based algorithms using both neural networks (NN) and reinforcement learning (RL). We further show that swarm steering can be achieved through broadcast cueing without direct communication.
The discussion will highlight different methods, algorithmic design, and practical implications for autonomous robotics, sensor networks, and other real-world multi-agent systems.