An Expanded Framework for Situation Control

There is an extensive body of literature on the topic of estimating situational states, in applications ranging from cyber-defense to military operations to traffic situations and autonomous cars. In the military/defense/intelligence literature, situation assessment seems to be the sine qua non for any research on surveillance and reconnaissance, command and control, and intelligence analysis. Virtually all of this work focuses on assessing the situation-at-the-moment; many if not most of the estimation techniques are based on Data and Information Fusion (DIF) approaches, with some recent schemes employing Artificial Intelligence (AI) and Machine Learning (ML) methods. But estimating and recognizing situational conditions is most often couched in a decision-making, action-taking context, implying that actions may be needed so that certain goal situations will be reached as a result of such actions, or at least that progress toward such goal states will be made. This context thus frames the estimation of situational states in the larger context of a control-loop, with a need to understand the temporal evolution of situational states, not just a snapshot at a given time. Estimating situational dynamics requires the important functions of situation recognition, situation prediction, and situation understanding that are also central to such an integrated estimation + action-taking architecture. The varied processes for all of these combined capabilities lie in a closed-loop “situation control” framework, where the core operations of a stochastic control process involve situation recognition—learning—prediction—situation “error” assessment—and action taking to move the situation to a goal state. We propose several additional functionalities for this closed-loop control process in relation to some prior work on this topic, to include remarks on the integration of control-theoretic principles. Expanded remarks are also made on the state of the art of the schemas and computational technologies for situation recognition, prediction and understanding, as well as the roles for human intelligence in this larger framework

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop