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Managers of US National Forests must decide what policy to apply for dealingwith lightning-caused wildfires. Conflicts among stakeholders (e.g., timbercompanies, home owners, and wildlife biologists) have often led to spiritedpolitical debates and even violent eco-terrorism. One way to transform theseconflicts into multi-stakeholder negotiations is to provide a high-fidelitysimulation environment in which stakeholders can explore the space ofalternative policies and understand the tradeoffs therein. Such an environmentneeds to support fast optimization of MDP policies so that users can adjustreward functions and analyze the resulting optimal policies. This paperassesses the suitability of SMAC---a black-box empirical function optimizationalgorithm---for rapid optimization of MDP policies. The paper describes fivereward function components and four stakeholder constituencies. It thenintroduces a parameterized class of policies that can be easily understood bythe stakeholders. SMAC is applied to find the optimal policy in this class forthe reward functions of each of the stakeholder constituencies. The resultsconfirm that SMAC is able to rapidly find good policies that make sense fromthe domain perspective. Because the full-fidelity forest fire simulator is fartoo expensive to support interactive optimization, SMAC is applied to asurrogate model constructed from a modest number of runs of the full-fidelitysimulator. To check the quality of the SMAC-optimized policies, the policiesare evaluated on the full-fidelity simulator. The results confirm that thesurrogate values estimates are valid. This is the first successful optimizationof wildfire management policies using a full-fidelity simulation. The samemethodology should be applicable to other contentious natural resourcemanagement problems where high-fidelity simulation is extremely expensive.

Fast Optimization of Wildfire Suppression Policies with SMAC