Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...
— The rapidly increasing complexity of tasks robotic systems are expected to carry out underscores the need for the development of motion planners that can take into account disc...
This paper explores the computational capacity of a novel local computational model that expands the conventional analogical and logical dynamic neural models, based on the charge ...
Physically-based modeling has been used in the past to support a variety of interactive modeling tasks including free-form surface design, mechanism design, constrained drawing, a...
Recently proposed usage control concept and models extend traditional access control models with features for contemporary distributed computing systems, including continuous acce...