Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Utility-based control for computer vision
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Active reduction of uncertainty in multisensor systems
Active reduction of uncertainty in multisensor systems
Coping with uncertainty in map learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A model for projection and action
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Learning dynamics: system identification for perceptually challenged agents
Artificial Intelligence
Reasoning MPE to multiply connected belief networks using message passing
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Efficient enumeration of instantiations in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Objection-based causal networks
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
High level path planning with uncertainty
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
| Hi-index | 0.00 |
A significant problem in designing mobile robot control systems involves coping with the uncertainty that arises in moving about in an unknown or partially unknown environment and relying on noisy or ambiguous sensor data to acquire knowledge about that environment. We describe a control system that chooses what activity to engage in next on the basis of expectations about how the information returned as a result of a given activity will improve its knowledge about the spatial layout of its environment. Certain of the higher-level components of the control system are specified in terms of probabilistic decision models whose output is used to mediate the behavior of lower-level control components responsible for movement and sensing. The control system is capable of directing the behavior of the robot in the exploration and mapping of its environment, while attending to the real-time requirements of navigation and obstacle avoidance.