Resumen
Algorithms for automated novelty detection and management are of growing interest but must address the inherent uncertainty from variations in non-novel environments while detecting the changes from the novelty. This paper expands on a recent unified framework to develop an operational theory for novelty that includes multiple (sub)types of novelty. As an example, this paper explores the problem of multi-type novelty detection in a 3D version of CartPole, wherein the cart Weibull-Open-World control-agent (WOW-agent) is confronted by different sub-types/levels of novelty from multiple independent agents moving in the environment. The WOW-agent must balance the pole and detect and characterize the novelties while adapting to maintain that balance. The approach develops static, dynamic, and prediction-error measures of dissimilarity to address different signals/sources of novelty. The WOW-agent uses the Extreme Value Theory, applied per dimension of the dissimilarity measures, to detect outliers and combines different dimensions to characterize the novelty. In blind/sequestered testing, the system detects nearly 100% of the non-nuisance novelties, detects many nuisance novelties, and shows it is better than novelty detection using a Gaussian-based approach. We also show the WOW-agent?s lookahead collision avoiding control is significantly better than a baseline Deep-Q-learning Networktrained controller.