K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management

Authors

  • Jonathan Ferrer-Mestres CSIRO
  • Thomas G. Dietterich Oregon State University
  • Olivier Buffet INRIA
  • Iadine Chades CSIRO

Keywords:

Environmental Sustainability

Abstract

In biodiversity conservation, adaptive management (AM) is the principal tool for decision making under uncertainty. AM problems are planning problems that can be modelled using Mixed Observability MDPs (MOMDPs). MOMDPs tackle decision problems where state variables are completely or partially observable. Unfortunately, MOMDP solutions (policy graphs) are too complex to be interpreted by human decision-makers. Here, we provide algorithms to solve K-N-MOMDPs, where K represents the maximum number of fully observable states and N represents the maximum number of alpha-vectors. Our algorithms calculate compact and more interpretable policy graphs from existing MOMDP models and solutions. We apply these algorithms to two computational sustainability applications: optimal release of bio-control agents to prevent dengue epidemics and conservation of the threatened bird species Gouldian finch. The methods dramatically reduce the number of states and alpha-vectors in MOMDP problems without significantly reducing their quality. The resulting policies have small policy graphs (4-6 nodes) that can be easily interpreted by human decision-makers.

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Published

2021-05-18

How to Cite

Ferrer-Mestres, J., Dietterich, T. G., Buffet, O., & Chades, I. (2021). K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14775-14784. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17735

Issue

Section

AAAI Special Track on AI for Social Impact