Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
Deductive, mode-estimation has become an essential component of robotic space systems, like NASA's deep space probes. Future robots will serve as components of large robotic ...
Theefficiency of algorithmsfor probabilistic inference in Bayesian networks can be improvedby exploiting independenceof causal influence. Thefactorized representation of independe...
Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into...
Alkis Simitsis, Panos Vassiliadis, Timos K. Sellis
We propose a new approach to value-directed belief state approximationfor POMDPs. The valuedirected model allows one to choose approximation methods for belief state monitoringtha...