Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
Knowledge discovery from temporal, spatial and spatiotemporal data is critical for climate change science and climate impacts. Climate statistics is a mature area. However, recent...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
We propose a novel approach to understanding
activities from their partial observations monitored through
multiple non-overlapping cameras separated by unknown time
gaps. In our...
This paper addresses the formal verification of diagnosis systems. We tackle the problem of diagnosability: given a partially observable dynamic system, and a diagnosis system obs...
Alessandro Cimatti, Charles Pecheur, Roberto Cavad...