A Shiny R app to solve POMDPs

In the last decade, artificial intelligence (AI) has increasingly been applied to help solve applied ecology problems. Partially observable Markov decision processes (POMDPs) are one such example. POMDPs have been applied in conservation, applied ecology and natural resource management to solve problems such as deciding when to stop managing or surveying threatened species that are difficult to detect. POMDP solvers are useful to find optimal sequential decisions under imperfect detection. However, POMDPs remain inaccessible to most applied ecologists.

We present the shiny r package smsPOMDP that solves the problem of ‘When to stop managing or surveying cryptic threatened species?’ (Chadès et al., 2008). We developed this package to address a common and challenging problem faced by conservation managers.

The smsPOMDP package and documentation are hosted at https://github.com/conservation‐decisions/smsPOMDP

In artificial intelligence, POMDPs are acknowledged as the Swiss army knife of decision models. However, POMDP’s application in applied ecology remains seldom despite repeated evidence of their flexibility. Our package smsPOMDP is fast and provides an entry point to further develop POMDP apps, contributing to further uptake of AI research to solve ecological problems.

Pascal, L, Memarzadeh, M, Boettiger, C, Lloyd, H, Chadès, I. A Shiny r app to solve the problem of when to stop managing or surveying species under imperfect detection. Methods Ecol Evol. 2020; 11: 1707– 1715. https://doi.org/10.1111/2041-210X.13501

A toolbox to solve stochastic dynamic programming problems in R, Matlab, SciLab or Octave

Our MDPToolbox is now published in Ecography. Thank you for supporting freely available programs. Please spread the word! The MDP/ SDP toolbox is now available in R, Matlab, SciLab and Octave. No excuses!

Stochastic dynamic programming (SDP) or Markov decision processes (MDP) are increasingly being used in ecology to find the best decisions over time and under uncertainty so that the chance of achieving an objective is maximised. To date, few programs are available to solve SDP/MDP. We present MDPtoolbox, a multi-platform set of functions to solve Markov decision problems (MATLAB, GNU Octave, Scilab and R). MDPtoolbox provides state-of-the-art and ready to use
algorithms to solve a wide range of MDPs. MDPtoolbox is easy to use, freely available and has been continuously improved since 2004. We illustrate how to use MDPtoolbox on a dynamic reserve design problem.

Chadès, I., Chapron, G., Cros, M.-J., Garcia, F. and Sabbadin, R. (2014), MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems. Ecography. doi: 10.1111/ecog.00888