Christopher A. Maynor

Papers

Total Citations

H-Index

About

Christopher A. Maynor is a researcher specializing in multi-robot systems, probabilistic planning, and decentralized decision-making under uncertainty. His work centers on developing rigorous mathematical frameworks that enable teams of autonomous robots to coordinate effectively even when operating with incomplete information about their environment. Maynor's most significant contribution is his development of a probabilistic framework grounded in Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs), a powerful model for synthesizing control policies in multi-robot systems where agents must cooperate to optimize shared objectives despite sensor limitations and environmental uncertainty. This framework addresses one of the fundamental challenges in robotics: how to design scalable, robust coordination strategies without relying on centralized control. His 2015 publication on this topic has garnered 88 citations, reflecting meaningful uptake within the robotics and artificial intelligence research communities. An earlier conference version of the work from 2014 further demonstrates his sustained commitment to advancing this problem. Maynor's contributions offer practical tools for researchers and engineers designing autonomous systems in domains such as search and rescue, exploration, and surveillance, where reliable decentralized coordination is essential.