  Real-world multi-agent planning problems cannot be solved using
decision-theoretic planning methods due to the exponential complexity. We
approximate firefighting in rescue simulation as a spatially distributed task
and model with multi-agent Markov decision process. We use recent approximation
methods for spatial task problems to reduce the model complexity. Our
approximations are single-agent, static task, shortest path pruning, dynamic
planning horizon, and task clustering. We create scenarios from RoboCup Rescue
Simulation maps and evaluate our methods on these graph worlds. The results
show that our approach is faster and better than comparable methods and has
negligible performance loss compared to the optimal policy. We also show that
our method has a similar performance as DCOP methods on example RCRS scenarios.
