A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

Online solvers for partially observable Markov decision processes have been applied to
problems with large discrete state spaces, but continuous state, action, and observation …

We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …

We consider the problem of dynamically allocating tasks to multiple agents under time
window constraints and task completion uncertainty. Our objective is to minimize the number …

Abstract Planning in Partially Observable Markov Decision Processes (POMDPs) inherently
gathers the information necessary to act optimally under uncertainties. The framework can …

Many real-world problems that require making optimal sequences of decisions under
uncertainty involve costs when the agent wishes to obtain information about its environment …

Online solvers for partially observable Markov decision processes have difficulty scaling to
problems with large action spaces. Monte Carlo tree search with progressive widening …

Mission-critical exploration of uncertain environments requires reliable and robust
mechanisms for achieving information gain. Typical measures of information gain such as …

Rather than augmenting rewards with penalties for undesired behavior, Constrained
Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing …

Autonomous agents operating in real-world scenarios frequently encounter uncertainty and
make decisions based on incomplete information. Planning under uncertainty can be …