Standard

Preallocation and Planning under Stochastic Resource Constraints. / de Nijs, Frits; Spaan, Matthijs; de Weerdt, Mathijs.

Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI , 2018.

Research output: Scientific - peer-reviewConference contribution

Harvard

de Nijs, F, Spaan, M & de Weerdt, M 2018, Preallocation and Planning under Stochastic Resource Constraints. in Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI .

APA

de Nijs, F., Spaan, M., & de Weerdt, M. (2018). Preallocation and Planning under Stochastic Resource Constraints. In Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI .

Vancouver

de Nijs F, Spaan M, de Weerdt M. Preallocation and Planning under Stochastic Resource Constraints. In Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI . 2018.

Author

de Nijs, Frits; Spaan, Matthijs; de Weerdt, Mathijs / Preallocation and Planning under Stochastic Resource Constraints.

Proceedings of the 32th AAAI Conference on Artificial Intelligence. AAAI , 2018.

Research output: Scientific - peer-reviewConference contribution

BibTeX

@inbook{33336524cc3e4e8c955e4a94102a72ff,
title = "Preallocation and Planning under Stochastic Resource Constraints",
author = "{de Nijs}, Frits and Matthijs Spaan and {de Weerdt}, Mathijs",
year = "2017",
month = "11",
booktitle = "Proceedings of the 32th AAAI Conference on Artificial Intelligence",
publisher = "AAAI",

}

RIS

TY - CHAP

T1 - Preallocation and Planning under Stochastic Resource Constraints

AU - de Nijs,Frits

AU - Spaan,Matthijs

AU - de Weerdt,Mathijs

PY - 2017/11/21

Y1 - 2017/11/21

N2 - Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.

AB - Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.

M3 - Conference contribution

BT - Proceedings of the 32th AAAI Conference on Artificial Intelligence

PB - AAAI

ER -

ID: 32554899