Max Robotics — Global Service Robot Directory + US Certification

About

Marcelo Luis Errecalde is a leading researcher in artificial intelligence, with a focus on defeasible reasoning, decision-making, and multi-agent systems. His major contributions lie in integrating defeasible logic programming (DeLP) with robotic and decision-making frameworks, enabling flexible, argument-based reasoning under uncertainty. His most cited work, "Decision Rules and Arguments in Defeasible Decision Making" (2008, 17 citations), introduces a model that allows agents to dynamically adjust decision policies by combining rules and arguments. Errecalde also pioneered the application of these theoretical models to physical systems, as seen in "An Application of Defeasible Logic Programming to Decision Making in a Robotic Environment" (2007, 14 citations) and "KheDeLP: A Framework to Support Defeasible Logic Programming for the Khepera Robots" (2006, 7 citations), bridging the gap between symbolic AI and embodied robotics. Additionally, his work on parallel reinforcement learning, "A parallel implementation of Q-learning based on communication with cache" (2002, 12 citations), demonstrates his versatility in tackling sequential decision problems. Errecalde’s research has significantly advanced the practical deployment of non-monotonic reasoning in autonomous systems, making him a key figure in the development of intelligent, adaptive agents.