Dynamic Agent Systems in the CoAX Binni 2002 Experiment

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.The goal of the international CoAX (Coalition Agents eXperiment) program was to demonstrate how agent systems could be used to provide agile and flexible command and control systems for coalition operations, and facilitate rapid integration of national C2 systems. The CoAX experiments modelled a coalition C4ISR system as a distributed, heterogeneous agent network using the DARPA CoABS (Control of Agent Based Systems) Grid infrastructure based on Java JINI technology. This paper outlines the CoAX Binni experiment which was held in October 2002 at the US Naval Warfare College, Newport RI. It describes the technology used in this experiment and the role of the ATTITUDE multi-agent architecture in the Australian component of the experiment. This involved logistics planning (and dynamic replanning) for a casualty evacuation from an Australian ship using BDI agents developed in the ATTITUDE architecture, and included interactions with Coalition medical and planning agents. Distributed agents were used to represent the various organisational entities involved in a simplified logistics model, and agent interactions with the Coalition C4ISR system were mediated by human operators using I-X Process Panels. This provided a semi-autonomous system, where human approval initiated further autonomous interactions between Coalition and Australian agents

The Nachtlichter app: a citizen science tool for documenting outdoor light sources in public space

The relationship between satellite based measurements of city radiance at night and the numbers and types of physical lights installed on the ground is not well understood. Here we present the "Nachtlichter app", which was developed to enable citizen scientists to classify and count light sources along street segments over large spatial scales. The project and app were co-designed: citizen scientists played key roles in the app development, testing, and recruitment, as well as in analysis of the data. In addition to describing the app itself and the data format, we provide a general overview of the project, including training materials, data cleaning, and the result of some basic data consistency checks