DDDAS Computational Model and Environments

DDDAS Predictions for Water Spills

Time based observations are the linchpin of improving predictions in any dynamic data driven application systems. Our predictions are based on solutions to differential equation models with unknown initial conditions and source terms. In this paper we want to simulate a waste spill by a water body, such as near an aquifer or in a river or bay. We employ sensors that can determine the contaminan...

Motivating Agents in Unreliable Environments: A Computational Model

The development of formal models for rational agents is mainly driven by the well-established BDI approach which divides an agent’s mental state into beliefs, desires, and intentions. In this paper, we argue that motivation as well has to be taken into account in order to allow for a flexible and proactive behavior of intelligent agents in unreliable environments. In our approach, motives take ...

Situation Awareness in Synthetic Environments: Towards a Computational Model

The simulation of the behaviour of autonomous entities in space is an interesting research method to advance our understanding of the interaction of human beings with the environment. Although rather complex cognitive models of human-level Situation Awareness (SA) in dynamic environments exist, computational approaches to represent the same remain at best ad-hoc – cognitively inadequate or bear...

Ambient Computational Environments

DDDAS-based Resilient Cyberspace (DRCS)