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Claire Montgomery

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RLDM Conference 2017 Conference Abstract

Visualizing High-Dimensional MDPs with Model-Free Monte Carlo*

Sean McGregor
Rachel Houtman
Claire Montgomery
Ronald Metoyer
Thomas Dietterich

Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajecto- ries and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This “off-policy Monte Carlo simulation” method works well when the state space has low dimension but fails as the dimension grows. This paper describes a method for factoring out some of the state and action variables so that MFMC can work in high- dimensional MDPs. The new method, MFMCi, is evaluated on a very challenging wildfire management MDP whose state space varies over more than 13 million state variables. The dimensionality of forestry domains makes MFMC unrealistic, but factorization reduces the stitching operation to 8 state features. The compact representation allows for high-fidelity visualization of policies.

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AAAI Conference 2013 Conference Paper

Large Landscape Conservation — Synthetic and Real-World Datasets

Bistra Dilkina
Katherine Lai
Ronan Le Bras
Yexiang Xue
Carla Gomes
Ashish Sabharwal
Jordan Suter
Kevin McKelvey

Biodiversity underpins ecosystem goods and services and hence protecting it is key to achieving sustainability. However, the persistence of many species is threatened by habitat loss and fragmentation due to human land use and climate change. Conservation efforts are implemented under very limited economic resources, and therefore designing scalable, cost-efficient and systematic approaches for conservation planning is an important and challenging computational task. In particular, preserving landscape connectivity between good habitat has become a key conservation priority in recent years. We give an overview of landscape connectivity conservation and some of the underlying graph-theoretic optimization problems. We present a synthetic generator capable of creating families of randomized structured problems, capturing the essential features of real-world instances but allowing for a thorough typical-case performance evaluation of different solution methods. We also present two large-scale real-world datasets, including economic data on land cost, and species data for grizzly bears, wolverines and lynx.

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AAAI Conference 2013 Conference Paper

Robust Network Design For Multispecies Conservation

Ronan Le Bras
Bistra Dilkina
Yexiang Xue
Carla Gomes
Kevin McKelvey
Michael Schwartz
Claire Montgomery

Our work is motivated by an important network design application in computational sustainability concerning wildlife conservation. In the face of human development and climate change, it is important that conservation plans for protecting landscape connectivity exhibit certain level of robustness. While previous work has focused on conservation strategies that result in a connected network of habitat reserves, the robustness of the proposed solutions has not been taken into account. In order to address this important aspect, we formalize the problem as a node-weighted bi-criteria network design problem with connectivity requirements on the number of disjoint paths between pairs of nodes. While in most previous work on survivable network design the objective is to minimize the cost of the selected network, our goal is to optimize the quality of the selected paths within a speciﬁed budget, while meeting the connectivity requirements. We characterize the complexity of the problem under different restrictions. We provide a mixed-integer programming encoding that allows for ﬁnding solutions with optimality guarantees, as well as a hybrid local search method with better scaling behavior but no guarantees. We evaluate the typical-case performance of our approaches using a synthetic benchmark, and apply them to a large-scale real-world network design problem concerning the conservation of wolverine and lynx populations in the U. S. Rocky Mountains (Montana).

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AAAI Conference 2011 Conference Paper

The Steiner Multigraph Problem: Wildlife Corridor Design for Multiple Species

Katherine Lai
Carla Gomes
Michael Schwartz
Kevin McKelvey
David Calkin
Claire Montgomery

The conservation of wildlife corridors between existing habitat preserves is important for combating the effects of habitat loss and fragmentation facing species of concern. We introduce the Steiner Multigraph Problem to model the problem of minimum-cost wildlife corridor design for multiple species with different landscape requirements. This problem can also model other analogous settings in wireless and social networks. As a generalization of Steiner forest, the goal is to ﬁnd a minimum-cost subgraph that connects multiple sets of terminals. In contrast to Steiner forest, each set of terminals can only be connected via a subset of the nodes. Generalizing Steiner forest in this way makes the problem NP-hard even when restricted to two pairs of terminals. However, we show that if the node subsets have a nested structure, the problem admits a ﬁxed-parameter tractable algorithm in the number of terminals. We successfully test exact and heuristic solution approaches on a wildlife corridor instance for wolverines and lynx in western Montana, showing that though the problem is computationally hard, heuristics perform well, and provably optimal solutions can still be obtained.

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