Preallocation and Planning Under Stochastic Resource Constraints

Abstract

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.

Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

“…Markov chains are established models for stochastic processes. Discrete-time Markov chains have been used to model environmental hazards (Tihanyi et al, 2021), resource constraints (de Nijs et al, 2018), disease spread (Gómez et al, 2010), and human task performance (Costen et al, 2022). However, many real world processes operate in continuous time.…”