Neuro-Symbolic Pump Scheduling for Safe and Cost-efficient Water Distribution Networks

AAMAS 2026 Workshop EMAS Submission19 Authors

Keywords: Water Distribution Networks, Neuro-Symbolic AI, Deep Reinforcement Learning, BDI Agents

TL;DR: Neuro-Symbolic AI optimises Water Networks for safety better than Deep Learning frameworks alone.

Abstract: The Pump Scheduling Problem is a highly challenging real-world control task in Water Distribution Networks (WDNs) that aims to minimise operational costs while meeting safety requirements (e.g., minimum and maximum allowable tank levels). Latest Deep Reinforcement Learning (DRL) techniques are effective for cost optimisation but can still violate safety constraints at deployment despite explicit safety considerations during training. Furthermore, evolving safety requirements (e.g., due to seasonal considerations) make retraining for minor safety specification changes disproportionately expensive. To address these challenges, we present a neuro-symbolic framework that pairs a pre-trained DRL agent with a symbolic Belief-Desire-Intention (BDI) agent for WDN safety supervision. Our implementation and preliminary empirical results demonstrate improved safety compliance over a DRL-only baseline while maintaining comparable cost performance.

Paper Type: Short paper

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Submission Number: 19