MOMDPs: a Solution for Modelling Adaptive Management Problems

Iadine Chadès 1, * Josie Carwardine 1 Tara Martin 2 Samuel Nicol 3 Régis Sabbadin 4 Olivier Buffet 5
* Corresponding author
2 Ecosystem Sciences
CSIRO Ecosystem Sciences
5 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : In conservation biology and natural resource management, adaptive management is an iterative process of improving management by reducing uncertainty via monitoring. Adaptive management is the principal tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a restricted Mixed Observability MDP called hidden model MDP (hmMDP). We demonstrate how to simplify the value function, the backup operator and the belief update computation. We show that, although a simplified case of POMDPs, hmMDPs are PSPACE-complete in the finite-horizon case. We illustrate the use of this model to manage a population of the threatened Gouldian finch, a bird species endemic to North- ern Australia. Our simple modelling approach is an important step towards efficient algorithms for solving adaptive management problems.
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Iadine Chadès, Josie Carwardine, Tara Martin, Samuel Nicol, Régis Sabbadin, et al.. MOMDPs: a Solution for Modelling Adaptive Management Problems. Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), Jul 2012, Toronto, Canada. ⟨hal-00755264⟩

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