The ability for an agent to reason under uncertainty is crucial for many planning applications, since an agent rarely has access to complete, error-free information about its envi...
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real ...
We introduce the patch transform, where an image is broken into non-overlapping patches, and modifications or constraints are applied in the "patch domain". A modified i...
Taeg Sang Cho, Moshe Butman, Shai Avidan, William ...
— In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot’s perf...
Christian Plagemann, Kristian Kersting, Patrick Pf...