We describe an approach to statistically verifying complex controllers. This approach is based on deriving practical Vapnik-Chervonenkis-style (VC) generalization bounds for binary classifiers with weighted loss. An important case is deriving bounds on the probability of false positive. We show how existing methods to derive bounds on classification error can be extended to derive similar bounds on the probability of false positive, as well as bounds in a decision-theoretic setting that allows tradeoffs between false negatives and false positives. We describe experiments using these bounds in statistically verifying computational properties of an iterative controller for an Organic Air Vehicle (OAV).
|Original language||English (US)|
|Number of pages||6|
|Journal||IJCAI International Joint Conference on Artificial Intelligence|
|State||Published - Dec 1 2005|
|Event||19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom|
Duration: Jul 30 2005 → Aug 5 2005