Geometric modeling of a nuclear environment
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2, 1999.
This paper is about the task-directed updating of an incomplete and inaccurate geometric model of a nuclear environment, using only robust radiation-resistant sensors installed on a robot that is remotely controlled by a human operator. In this problem, there are many sources of uncertainty and ambiguity. This paper proposes a probabilistic solution under Gaussian assumptions. Uncertainty is reduced with an estimator based on a Kalman filter. Ambiguity on the measurement-feature association is resolved by running a bank of those estimators in parallel, one for each plausible association. The residual errors of these estimators are used for hypothesis testing and for the calculation of a probability distribution over the remaining hypotheses. The best next sensing action is calculated as a Bayes decision with respect to a loss function that takes into account both the uncertainty on the current estimate, and the variance/precision required by the task.