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

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

The partially observable Markov decision process (POMDP) provides a general framework
for modeling an agent's decision process with state uncertainty, and online planning plays a …

This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi
optimistic optimization (VOO) and action progressive widening to partially observable …

Partially observable Markov decision processes (POMDPs) provide a flexible representation
for real-world decision and control problems. However, POMDPs are notoriously difficult to …

This work examines the hypothesis that partially observable Markov decision process
(POMDP) planning with human driver internal states can significantly improve both safety …

A human-centered robot needs to reason about the cognitive limitation and potential
irrationality of its human partner to achieve seamless interactions. This paper proposes an …

Solving partially observable Markov decision processes (POMDPs) with high dimensional
and continuous observations, such as camera images, is required for many real life robotics …

In this paper, we consider online planning in par-tially observable domains. Solving the
corresponding POMDP problem is a very challenging task, particularly in an online setting …

Civil and maritime engineering systems must be efficiently managed to control the failure
risk at an acceptable level as their performance is gradually degraded throughout the …