  In recent years, human casualties and damage to resources caused by emergent
incidents have become a serious problem worldwide. In this paper, we model the
emergency decision-making problem and use Multi-agent System (MAS) to solve the
problem that the decision speed cannot keep up with the spreading speed. MAS
can play an important role in the automated execution of these tasks to reduce
mission completion time. In this paper, we propose a P-MADDPG algorithm to
solve the emergency decision-making problem of emergent incidents, which
predicts the nodes where an incident may occur in the next time by GRU model
and makes decisions before the incident occurs, thus solving the problem that
the decision speed cannot keep up with the spreading speed. A simulation
environment was established for realistic scenarios, and three scenarios were
selected to test the performance of P-MADDPG in emergency decision-making
problems for emergent incidents: unmanned storage, factory assembly line, and
civil airport baggage transportation. Simulation results using the P-MADDPG
algorithm are compared with the greedy algorithm and the MADDPG algorithm, and
the final experimental results show that the P-MADDPG algorithm converges
faster and better than the other algorithms in scenarios of different sizes.
This shows that the P-MADDP algorithm is effective for emergency
decision-making in emergent incident.
