Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Tracking and data association
Planning and control
The data association problem when monitoring robot vehicles using dynamic belief networks
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Integration, Coordination and Control of Multi-Sensor Robot Systems
Integration, Coordination and Control of Multi-Sensor Robot Systems
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Plan Recognition in Stories and in Life
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Model-Based Influence Diagrams for Machine Vision
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Monitoring discrete environments using dynamic belief networks (robotics)
Monitoring discrete environments using dynamic belief networks (robotics)
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
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The trajectory of a robot is monitored in a restricted dynamic environment using light beam sensor data. We have a Dynamic Belief Network (DBN), based on a discrete model of the domain, which provides discrete monitoring analogous to conventional quantitative filter techniques. Sensor observations are added to the basic DBN in the form of specific evidence. However, sensor data is often partially or totally incorrect. We show how the basic DBN, which infers only an impossible combination of evidence, may be modified to handle specific types of incorrect data which may occur in the domain. We then present an extension to the DBN, the addition of an invalidating node, which models the status of the sensor as working or defective. This node provides a qualitative explanation of inconsistent data: it is caused by a defective sensot. The connection of successive instances of the invalidating node models the status of a sensor over time, allowing the DBN to handle both persistent and intermittent faults.