We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several ...
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. denite clause programs containing probabilistic facts with a ...
Abstract. The Web has been rapidly “deepened” with the prevalence of databases online. On this “deep Web,” numerous sources are structured, providing schema-rich data– Th...
This paper presents Spinach, a new simulator toolset specifically designed to target programmable network interface architectures. Spinach models both system components that are ...
We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned obje...