Abstract-In this paper we present an account of the problems faced by a mobile robot given an incomplete tour of an unknown environment, and introduce a collection of techniques which can generate successful behaviour even in the presence of such problems. Underlying our approach is the principle that an autonomous system must be motivated to act to gather new knowledge, and to validate and correct existing knowledge. This principle is embodied in Dora, a mobile robot which features the aforementioned techniques: shared representations, non-monotonic reasoning, and goal generation and management. To demonstrate how well this collection of techniques work in real-world situations we present a comprehensive analysis of the Dora system's performance over multiple tours in an indoor environment. In this analysis Dora successfully completed 18 of 21 attempted runs, with all but 3 of these successes requiring one or more of the integrated techniques to recover from problems.

This paper describes how multiple independent observation tasks can be scheduled for an autonomous vehicle. Presented with large numbers of tasks, of differing reward levels, a vehicle has to evaluate the best schedule to execute given a limited time to both plan and act. A meta-management framework acting on top of an anytime scheduler analyses the problem and the progress made in generating solutions to identify when to stop planning and start executing. We compare a probabilistic management technique with active monitoring of the current execution reward and conclude that in this case detecting a local maxima in the predicted reward is the most effective policy.