— Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a...
Andrea Censi, Daniele Calisi, Alessandro De Luca, ...
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
In the unconditionally reliable message transmission (URMT) problem, two non-faulty players, the sender S and the receiver R are part of a synchronous network modeled as a directe...
Adaptive applications have computational workloads and communication patterns which change unpredictably at runtime, requiring dynamic load balancing to achieve scalable performan...
Hongzhang Shan, Jaswinder Pal Singh, Leonid Oliker...
This paper proposes a novel representation of the free space of mobile robot by distinct, non-overlapping regions called Edge Visibility Regions (EVRs). An algorithm to partition ...