🤖 AI Summary
This work addresses the challenge of efficiently replanning large-scale disturbances caused by physical couplings in industrial multi-agent systems under communication constraints and delays. The authors propose CASCADE, a novel mechanism that explicitly models communication range as an auditable and scalable coordination dimension. By decoupling a unified agent substrate from a scoped interaction layer, CASCADE enables dynamically triggered cascading coordination. Agents make local decisions conditioned on their roles using a shared knowledge base and employ lightweight contract primitives to expand coordination scope on demand. Evaluated in manufacturing and supply chain disruption scenarios, the approach achieves a superior trade-off among replanning quality, latency, and communication overhead, significantly enhancing robustness under uncertainty.
📝 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.