Dynamic Resource Allocation for Optimizing Population Diffusion

This paper addresses adaptive conservation planning, where the objective is to maximize the population spread of a species by allocating limited resources over time to conserve land parcels. This problem is characterized by having highly stochastic exogenous events (population spread), a large action branching factor (number of allocation options) and state space, and the need to reason about numeric resources. Together these characteristics render most existing AI planning techniques ineffective. The main contribution of this paper is to design and evaluate an online planner for this problem based on Hindsight Optimization (HOP), a technique that has shown promise in other stochastic planning problems. Unfortunately, standard implementations of HOP scale linearly with the number of actions in a domain, which is not feasible for conservation problems such as ours. Thus, we develop a new approach for computing HOP policies based on mixed-integer programming and dual decomposition. Our experiments on synthetic and real-world scenarios show that this approach is effective and scalable compared to existing alternatives.