Partially observable Markov decision processes (POMDPs) provide a principled framework
for modeling an agent's decision-making problem when the agent needs to consider noisy …

Bayesian approaches provide a principled solution to the exploration-exploitation trade-off
in Reinforcement Learning. Typical approaches, however, either assume a fully observable …

In the manufacturing industry, the era of mass customization has arrived. Combining the
complementary strengths of humans and robots will allow to cope with growing product …

We address the problem of learning relationships on state variables in Partially Observable
Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we …

In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty
about one or more hidden variables. For example, a mobile robot takes sensory actions to …

In this paper, the multi-goal motion planning problem of an environment with some
background information about its map is addressed in detail. The motion planning goal is to …

We consider controlling a graph-based Markov decision process (GMDP) with a control
capacity constraint given only uncertain measurements of the underlying state. We also …

We show how to integrate a variable elimination approach to solving influence diagrams
with a value iteration approach to solving finite-horizon partially observable Markov decision …

Interactive dynamic influence diagrams (I-DIDs) are recognized graphical models for
sequential multiagent decision making under uncertainty. They represent the problem of …

Partial observability, or the inability of an agent to fully observe the state of its environment,
exists in many real-world problem domains. However, most cognitive architectures do not …