Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
Distance rationalizability is an intuitive paradigm for developing and studying voting rules: given a notion of consensus and a distance function on preference profiles, a ration...
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
Recognition of signs in sentences requires a training
set constructed out of signs found in continuous sentences.
Currently, this is done manually, which is a tedious process.
I...
This paper presents a target tracking framework for unstructured
crowded scenes. Unstructured crowded scenes
are defined as those scenes where the motion of a crowd
appears to b...