CASCADE: Cascaded Scoped Communication for Multi-Agent Re-planning in Disrupted Industrial Environments

TL;DR

CASCADE introduces a budgeted multi-agent re-planning approach that explicitly manages communication scope, improving robustness and trade-offs in disrupted industrial environments.

Contribution

It proposes a novel mechanism for explicit, scalable communication scope control in multi-agent re-planning under disruption.

Findings

Explicit scope control yields better quality-latency-communication trade-offs.

The mechanism improves robustness under uncertainty.

Evaluation on manufacturing and supply-chain disruptions demonstrates effectiveness.

Abstract

Industrial disruption replanning demands multi-agent coordination under strict latency and communication budgets, where disruptions propagate through tightly coupled physical dependencies and rapidly invalidate baseline schedules and commitments. Existing coordination schemes often treat communication as either effectively free (broadcast-style escalation) or fixed in advance (hand-tuned neighborhoods), both of which are brittle once the disruption footprint extends beyond a local region. We present \CASCADE, a budgeted replanning mechanism that makes communication scope explicit and auditable rather than fixed or implicit. Each agent maintains an explicit knowledge base, solves role-conditioned local decision problems to revise commitments, and coordinates through lightweight contract primitives whose footprint expands only when local validation indicates that the current scope is insufficient. This design separates a unified agent substrate (Knowledge Base / Decision Manager / Communication Manager) from a scoped interaction layer that controls who is contacted, how far coordination propagates, and when escalation is triggered under explicit budgets. We evaluate \CASCADE on disrupted manufacturing and supply-chain settings using unified diagnostics intended to test a mechanism-design claim -- whether explicit scope control yields useful quality-latency-communication trade-offs and improved robustness under uncertainty -- rather than to provide a complete algorithmic ranking.