This paper introduces power default reasoning (PDR), a framework for nonmonotonic reasoning based on the domain-theoretic idea of modeling default rules with partial-information i...
In this paper, we apply an evolutionary algorithm to learning behavior on a novel, interesting task to explore the general issue of learning e ective behaviors in a complex enviro...
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
—We consider a widely applicable model of resource allocation where two sequences of events are coupled: on a continuous time axis (t), network dynamics evolve over time. On a di...
Alexandre Proutiere, Yung Yi, Tian Lan, Mung Chian...
This paper presents a loosely coupled service-composition paradigm. This paradigm employs a distributed data flow that differs markedly from centralized information flow adopted b...
David Liu, Jun Peng, Kincho H. Law, Gio Wiederhold