Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Planning and control
Utility-based control for computer vision
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Mobile robot localization using active sensing based on Bayesian network inference
Robotics and Autonomous Systems
Probabilistic Methods for Financial and Marketing Informatics
Probabilistic Methods for Financial and Marketing Informatics
| Hi-index | 0.00 |
The application of Bayesian decision theory as a framework for designing high-level robotic control systems is discussed. The approach to building planning and control systems integrates sensor fusion, prediction, and sequential decision making. The system explicitly uses the value of sensor information as well as the value of actions that facilitate further sensing. A stochastic decision model and a model for mobile-target localization used in the control system are described. A control system implemented to drive a small mobile robot equipped with eight sonar transducers with a maximum range of six meters and a visual processing system capable of identifying moving targets in its visual field and reporting their motion relative to the robot is also discussed.