CASCADE: Cascaded Scoped Communication for Multi-Agent Re-planning in Disrupted Industrial Environments
This work addresses the challenge of efficient and robust multi-agent replanning for industrial applications, though it appears incremental by building on existing coordination schemes with a focus on explicit scope control.
The paper tackles the problem of multi-agent coordination in disrupted industrial environments by introducing CASCADE, a budgeted replanning mechanism that uses explicit communication scope control, resulting in improved trade-offs between quality, latency, and communication, as well as enhanced robustness under uncertainty.
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.