Addressing structural and observational uncertainty in resource management.

Most natural resource management and conservation problems are plagued with high levels of uncertainties, which make good decision making difficult. Although some kinds of uncertainties are easily incorporated into decision making, two types of uncertainty present more formidable difficulties. The first, structural uncertainty, represents our imperfect knowledge about how a managed system behaves. The second, observational uncertainty, arises because the state of the system must be inferred from imperfect monitoring systems. The former type of uncertainty has been addressed in ecology using Adaptive Management (AM) and the latter using the Partially Observable Markov Decision Processes (POMDP) framework. Here we present a unifying framework that extends standard POMDPs and encompasses both standard POMDPs and AM. The approach allows any system variable to be observed or not observed and uses any relevant observed variable to update beliefs about unknown variables and parameters. This extends standard AM, which only uses realizations of the state variable to update beliefs and extends standard POMDP by allowing more general stochastic dependence among the observable variables and the state variables. This framework enables both structural and observational uncertainty to be simultaneously modeled. We illustrate the features of the extended POMDP framework with an example.

[1]  D. Tomberlin,et al.  Endangered Seabird Habitat Management as a Partially Observable Markov Decision Process , 2010, Marine Resource Economics.

[2]  Michael C. Fu,et al.  Solving Continuous-State POMDPs via Density Projection , 2010, IEEE Transactions on Automatic Control.

[3]  Cindy E. Hauser,et al.  Resource allocation for efficient environmental management. , 2010, Ecology letters.

[4]  B. Williams.  Resolving structural uncertainty in natural resources management using POMDP approaches , 2011 .

[5]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Nikos A. Vlassis,et al.  Perseus: Randomized Point-based Value Iteration for POMDPs , 2005, J. Artif. Intell. Res..

[7]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[8]  F. Johnson,et al.  Uncertainty and the Adaptive Management of Waterfowl Harvests , 1996 .

[9]  B K Williams,et al.  Adaptive optimization and the harvest of biological populations. , 1996, Mathematical biosciences.

[10]  C. T. Moore,et al.  Uncertainty and the management of mallard harvests , 1997 .

[11]  M. Conroy,et al.  Analysis and Management of Animal Populations , 2002 .

[12]  C. S. Holling.  Adaptive Environmental Assessment and Management , 2005 .

[13]  Li Ling Ko,et al.  Structured Parameter Elicitation , 2010, AAAI.

[14]  I. Chades,et al.  General rules for managing and surveying networks of pests, diseases, and endangered species , 2011, Proceedings of the National Academy of Sciences.

[15]  B. White.  An economic analysis of ecological monitoring , 2005 .

[16]  Brendan A. Wintle,et al.  When to stop managing or surveying cryptic threatened species , 2008, Proceedings of the National Academy of Sciences.

[17]  Michael R. Springborn,et al.  A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources , 2013 .

[18]  I. Chades,et al.  Optimally managing under imperfect detection: A method for plant invasions , 2011 .

[19]  Cindy E. Hauser,et al.  Experimental or precautionary? Adaptive management over a range of time horizons , 2007 .

[20]  G. Monahan.  State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 1982 .

[21]  Byron K Williams,et al.  Passive and active adaptive management: approaches and an example. , 2011, Journal of environmental management.

[22]  James D. Nichols,et al.  Conservation in the face of climate change: The roles of alternative models, monitoring, and adaptation in confronting and reducing uncertainty , 2011 .

[23]  Olivier Buffet,et al.  A Closer Look at MOMDPs , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[24]  Cindy E. Hauser,et al.  Active adaptive conservation of threatened species in the face of uncertainty. , 2010, Ecological applications : a publication of the Ecological Society of America.

[25]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[26]  Olivier Buffet,et al.  MOMDPs: A Solution for Modelling Adaptive Management Problems , 2012, AAAI.

[27]  Carl J. Walters,et al.  Adaptive Management of Renewable Resources , 1986 .

[28]  Peter W J Baxter,et al.  Optimal eradication: when to stop looking for an invasive plant. , 2006, Ecology letters.

[29]  David Hsu,et al.  POMDPs for robotic tasks with mixed observability , 2009, Robotics: Science and Systems.

[30]  Julien Martin,et al.  An Adaptive‐Management Framework for Optimal Control of Hiking Near Golden Eagle Nests in Denali National Park , 2011, Conservation biology : the journal of the Society for Conservation Biology.

[31]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[32]  Julien Martin,et al.  Structured decision making as a proactive approach to dealing with sea level rise in Florida , 2011 .

[33]  B. White,et al.  A Bioeconomic Analysis of the Duration of Conservation Contracts , 2007 .

[34]  Alexei Makarenko,et al.  Parametric POMDPs for planning in continuous state spaces , 2006, Robotics Auton. Syst..

[35]  Geoffrey J. Gordon,et al.  Finding Approximate POMDP solutions Through Belief Compression , 2011, J. Artif. Intell. Res..

[36]  George E. Monahan,et al.  A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 2007 .

[37]  David Hsu,et al.  SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces , 2008, Robotics: Science and Systems.

[38]  David Hsu,et al.  Planning under Uncertainty for Robotic Tasks with Mixed Observability , 2010, Int. J. Robotics Res..

[39]  Robert G. Haight,et al.  Optimal control of an invasive species with imperfect information about the level of infestation , 2010 .

[40]  Deb Roy,et al.  Connecting language to the world , 2005, Artif. Intell..

[41]  Cindy E. Hauser,et al.  How we value the future affects our desire to learn. , 2008, Ecological applications : a publication of the Ecological Society of America.

[42]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[43]  Daniel E. Lane,et al.  A Partially Observable Model of Decision Making by Fishermen , 1989, Oper. Res..

[44]  I. Chades,et al.  Allocating conservation resources between areas where persistence of a species is uncertain. , 2011, Ecological applications : a publication of the Ecological Society of America.

[45]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[46]  Lucile Marescot,et al.  Complex decisions made simple: a primer on stochastic dynamic programming , 2013 .