Michael Kaisers

Centrum Wiskunde & Informatica, Maastricht University

Papers

Total Citations

369

H-Index

About

Michael Kaisers is a leading researcher in multi-agent learning and swarm intelligence, with a focus on developing adaptive algorithms for complex, dynamic systems. His seminal survey, "Evolutionary Dynamics of Multi-Agent Learning: A Survey" (2015), with 287 citations, provides a foundational framework for understanding how autonomous agents interact in nondeterministic environments, impacting fields like automated financial markets, smart grids, and robotics. Kaisers’ major contributions include the "Frequency adjusted multi-agent Q-learning" (2010, 67 citations), a crucial method for enabling adaptive behavior in systems with multiple entities, from economic auctions to swarm robotics. He also pioneered embodied swarm intelligence with his bee-inspired foraging algorithm (2011), demonstrating its effectiveness, scalability, and adaptability in physical robots—a notable achievement that bridges simulation and real-world application. His work has garnered over 369 citations, highlighting its influence on multi-agent systems and robotics. Kaisers’ research continues to shape how we design autonomous systems that learn and cooperate in unpredictable environments, making him a key figure in advancing AI and swarm robotics.