Heuristic Guidance for Forward-Chaining Planning with Numeric Uncertainty. Marinescu, L. E. & Coles, A. In
Heuristic Guidance for Forward-Chaining Planning with Numeric Uncertainty [link]Paper  abstract   bibtex   
Uncertainty hinders many interesting applications of planning – it may come in the form of sensor noise, unpredictable environments, or known limitations in problem models. In this paper we explore heuristic guidance for forward-chaining planning with continuous random variables, while ensuring a probability of plan success. We extend the Metric Relaxed Planning Graph heuristic to capture a model of uncertainty, providing better guidance in terms of heuristic estimates and dead-end detection. By tracking the accumulated error on numeric values, our heuristic is able to check if preconditions in the planning graph are achievable with a sufficient degree of confidence; it is also able to consider acting to reduce the accumulated error. Results indicate that our approach offers improvements in performance compared to prior work where a less-informed relaxation was used.
@inproceedings {icaps16-78,
    track    = {​Main Track},
    title    = {Heuristic Guidance for Forward-Chaining Planning with Numeric Uncertainty},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13186},
    author   = {Liana E. Marinescu and  Andrew Coles},
    abstract = {Uncertainty hinders many interesting applications of planning -- it may come in the form of sensor noise, unpredictable environments, or known limitations in problem models. In this paper we explore heuristic guidance for forward-chaining planning with continuous random variables, while ensuring a probability of plan success. We extend the Metric Relaxed Planning Graph heuristic to capture a model of uncertainty, providing better guidance in terms of heuristic estimates and dead-end detection. By tracking the accumulated error on numeric values, our heuristic is able to check if preconditions in the planning graph are achievable with a sufficient degree of confidence; it is also able to consider acting to reduce the accumulated error. Results indicate that our approach offers improvements in performance compared to prior work where a less-informed relaxation was used.},
    keywords = {Classical planning,Probabilistic planning; MDPs and POMDPs,Planning under (non-probabilistic) uncertainty}
}
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