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...
We propose a frameworkfor robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGolog model a...
Craig Boutilier, Raymond Reiter, Mikhail Soutchans...
Generating 3D models of real world objects is a common task during development of any augmented reality application. This paper describes how ProFORMA (Probabilistic Feature-based...
A problem of planning for cooperative teams under uncertainty is a crucial one in multiagent systems. Decentralized partially observable Markov decision processes (DECPOMDPs) prov...
Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic...