Towards Uncertainty Reduction Tactics for Behavior Adaptation

An autonomous system must continuously adapt its behavior to its context in order to fulfill its goals in dynamic environments. Obtaining information about the context, however, often leads to partial knowledge, only, with a high degree of uncertainty. Enabling the systems to actively reduce these uncertainties at run-time by performing additional actions, such as changing a mobile robot’s position to improve the perception with additional perspectives, can increase the systems’ performance. However, incorporating these techniques by adapting behavior plans is not trivial as the potential benefit of such so-called tactics highly depends on the specific context. In this paper, we present an analysis of the performance improvement that can theoretically be achieved with uncertainty reduction tactics. Furthermore, we describe a modeling methodology based on probabilistic data types that makes it possible to estimate the suitability of a tactic in a situation. This methodology is the first step towards enabling autonomous systems to use uncertainty reduction in practice and to plan behavior with more optimal performance.