  Managers of US National Forests must decide what policy to apply for dealing
with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber
companies, home owners, and wildlife biologists) have often led to spirited
political debates and even violent eco-terrorism. One way to transform these
conflicts into multi-stakeholder negotiations is to provide a high-fidelity
simulation environment in which stakeholders can explore the space of
alternative policies and understand the tradeoffs therein. Such an environment
needs to support fast optimization of MDP policies so that users can adjust
reward functions and analyze the resulting optimal policies. This paper
assesses the suitability of SMAC---a black-box empirical function optimization
algorithm---for rapid optimization of MDP policies. The paper describes five
reward function components and four stakeholder constituencies. It then
introduces a parameterized class of policies that can be easily understood by
the stakeholders. SMAC is applied to find the optimal policy in this class for
the reward functions of each of the stakeholder constituencies. The results
confirm that SMAC is able to rapidly find good policies that make sense from
the domain perspective. Because the full-fidelity forest fire simulator is far
too expensive to support interactive optimization, SMAC is applied to a
surrogate model constructed from a modest number of runs of the full-fidelity
simulator. To check the quality of the SMAC-optimized policies, the policies
are evaluated on the full-fidelity simulator. The results confirm that the
surrogate values estimates are valid. This is the first successful optimization
of wildfire management policies using a full-fidelity simulation. The same
methodology should be applicable to other contentious natural resource
management problems where high-fidelity simulation is extremely expensive.
