Sensitivity Analysis for Dynamic Control of PSTNs with Skewed Distributions
Keywords:Human-aware planning and scheduling, Applications and case studies of plannin and scheduling techniques
AbstractProbabilistic Simple Temporal Networks (PSTN) facilitate solving many interesting scheduling problems by characterizing uncertain task durations with unbounded probabilistic distributions. However, most current approaches assess PSTN performance using normal or uniform distributions of temporal uncertainty. This paper explores how well such approaches extend to families of non-symmetric distributions shown to better represent the temporal uncertainty introduced by, e.g., human teammates by building new PSTN benchmarks. We also build probability-aware variations of current approaches that are more reactive to the shape of the underlying distributions. We empirically evaluate the original and modified approaches over well-established PSTN datasets. Our results demonstrate that alignment between the planning model and reality significantly impacts performance. While our ideas for augmenting existing algorithms to better account for human-style uncertainty yield only marginal gains, our results surprisingly demonstrate that existing methods handle positively-skewed temporal uncertainty better.
How to Cite
Chen, R., Ma, Y., Wu, S., & Boerkoel Jr., J. C. (2023). Sensitivity Analysis for Dynamic Control of PSTNs with Skewed Distributions. Proceedings of the International Conference on Automated Planning and Scheduling, 33(1), 95-99. https://doi.org/10.1609/icaps.v33i1.27183