This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence. At its heart, the software approach relies on probabilistic computation, on-line learning, and any-time algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot's
Children often learn new problem-solving strategies by observing examples of other people's problem-solving. When children learn a new strategy through observation and also explain the new strategy to themselves, they generalize the strategy more widely than children who learn a new strategy but do not explain. We tested three hypothesized mechanisms through which explanations might facilitate strategy generalization: more accurate recall of the new strategy's procedures; increased selection of the new strategy over competing strategies; or more effective management of the new strategy's goal