CASCADE: Cascaded Scoped Communication for Multi-Agent Re-planning in Disrupted Industrial Environments
Organizations: State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), Beijing, China
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.
Figures & tables
| Setting | System and disruption regime | Coordination | Main reported signals / protocol |
|---|---|---|---|
| Manufacturing | Modified Intel Mini-Fab; two product types with fixed process routes; disruptions are machine breakdowns with operation-time uncertainty. | Centralized vs. CASCADE | Cycle time, communication , runtime ; incident and risk statistics in the disruption-aware case; 5 trials. |
| Supply chain | Automotive cockpit supply-chain network. Two disruption regimes: (i) single supplier-loss on the nominal flow plan; (ii) lead-time disruption with different added time attributes. | Centralized vs. CASCADE ; neutral vs. averse | Overage cost , communication , and footprint proxies ; delayed-product distribution and cost/lateness . |
| Panel A: Manufacturing robustness | ||||
| Metric | With risk | Without | % | |
| # damaged products | 3.80 1.10 | 7.00 2.12 | -3.20 | -45.7% |
| # broken machines | 4.52 1.23 | 7.84 2.49 | -3.32 | -42.3% |
| # rescheduled processes | 4.05 1.19 | 7.20 2.47 | -3.15 | -43.8% |
| Peak risk value | 0.02 0.04 | 0.03 0.04 | -0.01 | -33.3% |
| Average risk value | 0.03 0.02 | 0.06 0.04 | -0.03 | -50.0% |
Multi-Agent Coordination
Multi-agent coordination studies how autonomous agents align actions, share information, and divide work to achieve individual or collective goals.
See all 511 papers