In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...
− In a clustered, multi-hop sensor network, a large number of inexpensive, geographically-distributed sensor nodes each use their observations of the environment to make local ha...
The constraint paradigm provides powerful concepts to represent and solve different kinds of planning problems, e. g. factory scheduling. Factory scheduling is a demanding optimiz...
Effective supply chain distribution network design needs to consider various performance dimensions and product characteristics. Recently, researchers have begun to realize that t...
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...