Alexander Novoselsky

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About

Alexander Novoselsky is a pioneering researcher at the intersection of non-classical logic and multi-agent systems. His work fundamentally rethinks how autonomous agents make collective decisions, particularly when faced with asymmetric information or perception bias. Novoselsky’s most significant contribution is the development of a novel implementation of non-commutative logic for group decision-making. By extending traditional uninorm and absorbing norm aggregators with an asymmetry parameter, he has created a mathematical framework that captures the real-world reality that the order of opinions matters—a breakthrough for swarming robotics and distributed AI. His foundational 2019 paper on multi-robot systems and swarming laid the groundwork for these later advances. With over a dozen citations on his core 2023 work alone, Novoselsky is gaining recognition for providing a rigorous, formal tool to model how groups of agents can reach consensus even when individual perceptions are skewed or biased. This work has immediate implications for autonomous drone swarms, sensor networks, and any system where decentralized agents must make high-stakes decisions without a central leader.