Partially observable Markov decision processes (POMDPs) are a convenient mathematical
model to solve sequential decision‐making problems under imperfect observations. Most …

Managing the biodiversity extinction crisis requires wise decision-making processes able to
account for the limited resources available. In most decision problems in conservation …

Abstract Partially Observable Markov Decision Processes (POMDPs) are powerful models
for planning under uncertainty in partially observable domains. However, computing optimal …

Partially Observable Markov Decision Processes (POMDPs) are a popular formalism for
sequential decision making in partially observable environments. Since solving POMDPs to …

Abstract Markov Decision Processes (MDPs) are employed to model sequential decision-
making problems under uncertainty. Traditionally, algorithms to solve MDPs have focused …

In the last decade, artificial intelligence (AI) has increasingly been applied to help solve
applied ecology problems. Partially observable Markov decision processes (POMDPs) are …

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 …

Optimal policies for partially observed Markov decision processes (POMDPs) are history-
dependent: Decisions are made based on the entire history of observations. Memoryless …

In many fields in computational sustainability, applications of POMDPs are inhibited by the
complexity of the optimal solution. One way of delivering simple solutions is to represent the …

This paper presents a review of the existing publications using computational intelligence
techniques in applications to sustainability development. Computational intelligence is the …