C4.5: programs for machine learning
C4.5: programs for machine learning
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Continuous case-based reasoning
Artificial Intelligence
Artificial intelligence and mobile robots: case studies of successful robot systems
Artificial intelligence and mobile robots: case studies of successful robot systems
Map learning and high-speed navigation in RHINO
Artificial intelligence and mobile robots
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
An Behavior-based Robotics
Robot Motion Planning
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Decision Tree Confidence Factors for Multiagent Control
RoboCup-97: Robot Soccer World Cup I
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
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Most state-of-the-art navigation systems for autonomous service robots decompose navigation into global navigation planning and local reactive navigation. While the methods for navigation planning and local navigation are well understood, the plan execution problem, the problem of how to generate and parameterize local navigation tasks from a given navigation plan, is largely unsolved. This article describes how a robot can autonomously learn to execute navigation plans. We formalize the problem as a Markov Decision Problem (MDP), discuss how it can be simplified to make its solution feasible, and describe how the robot can acquire the necessary action models. We show, both in simulation and on a RWI B21 mobile robot, that the learned models are able to produce competent navigation behavior.