Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
The lack of structure in the content of email messages makes it very hard for data channelled between the sender and the recipient to be correctly interpreted and acted upon. As a...
Simon Scerri, Myriam Mencke, Brian Davis, Siegfrie...
— In this paper, navigation and control of autonomous mobile unicycle robots in a complex and partially known obstacleridden environment is considered. The unicycle dynamic model...
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...