Probabilistic Methods for State Estimation in Robotics
The eld of Arti cial Intelligence (AI) is currently undergoing a transition. While in the eighties, rule-based and logical representations were the representation of choice in the majority of AI systems, in recent years various researchers have explored alternative representational frameworks, which emphasis on frameworks that enable systems to represent and handle uncertainty. Out of those, probabilistic methods (and speci cally Bayesian methods) have probably been analyzed most thoroughly and applied most successfully in a variety of problem domains.