AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
HART '97 Proceedings of the International Workshop on Hybrid and Real-Time Systems
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Efficient solution techniques for disjunctive temporal reasoning problems
Artificial Intelligence
Prottle: a probabilistic temporal planner
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Temporal dynamic controllability revisited
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
Dynamic control of plans with temporal uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Journal of Scheduling
Modelling mixed discrete-continuous domains for planning
Journal of Artificial Intelligence Research
Strengthening schedules through uncertainty analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Hi-index | 0.00 |
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.