Proceedings of the AAAI Conference on Artificial Intelligence, 21
Reasoning about Plans and Actions
This paper considers the problem of stochastic robustness testing for plans. As many authors have observed, unforeseen execution-time variations, both in the effects of actions and in the times at which they occur, can result in a plan failing to execute correctly even when it is valid with respect to a domain model. In this paper we contrast the validation of a plan with respect to a domain model, confirming soundness, with the validation with respect to an execution model, which we call robustness. We describe a Monte Carlo probing strategy that takes a hypothesis testing approach to confirming the robustness of a plan. An important contribution of the work is that we draw links between the robustness of plans and the notion of the "fuzzy" robustness of traces through timed hybrid automata, introduced by Gupta et al. We show that robustness depends on the metric used to define the set of plans that are probed, and that the most appropriate metric depends on the capabilities of the executive and the way in which it will interpret and execute the plan.