Speaker:Alexander Artikis

Date:16/03/2012

University:NCSR Demokritos

Room :A56

Time:4:00pm (coffee: 3:30)

Abstract:

In the field of complex event processing, among others, there is a need for computational frameworks supporting real-time reasoning, reasoning under uncertainty and automated knowledge construction. I will present a dialect of the Event Calculus that meets these requirements. The Event Calculus is a logic programming language for representing and reasoning about events and their effects. Our dialect includes novel caching techniques that allow for efficient temporal reasoning, scalable to large data streams. Furthermore, when ported to probabilistic frameworks, it may support various types of uncertainty, such noisy data streams and imprecise knowledge. To avoid the time-consuming, error-prone process of manual knowledge construction, I will present techniques for incremental structure learning that take advantage of large datasets. The Event Calculus dialect will be illustrated with the use of two real-world applications: complex event recognition for city transport management and public space surveillance.