Robust tracking of abrupt motion is a challenging task
in computer vision due to the large motion uncertainty. In
this paper, we propose a stochastic approximation Monte
Carlo (...
We present an approach for recovery from perceptual failures, or more precisely anchoring failures. Anchoring is the problem of connecting symbols representing objects to sensor da...
Abdelbaki Bouguerra, Lars Karlsson, Alessandro Saf...
Techniques for plan recognition under uncertainty require a stochastic model of the plangeneration process. We introduce probabilistic state-dependent grammars (PSDGs) to represen...
We propose an online algorithm for planning under uncertainty in multi-agent settings modeled as DEC-POMDPs. The algorithm helps overcome the high computational complexity of solv...
Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, ...
Daniel Golovin, Andreas Krause, Beth Gardner, Sara...