<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental Validation Of Metamodels For Intelligent Agents In Conflict</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">95-103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In previous papers, the authors have described a theoretical approach to the development of mathematical meta-models, which aim to capture the emergent behaviour of intelligent agent-based constructive simulation models of military conflict. These intelligent agents capture the process of C4ISR (Command, Control, Communications, Computers, Intelligence Surveillance and Reconnaissance) in such agent-based simulation models. In this paper, the authors present both historical evidence and evidence from experiments using cellular automata models that support hypotheses derived from their theory.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">95</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">James Moffat</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Command and Control in the Information Age – Representing its Impact</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">London, UK: The Stationery Office</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Phase changes in Meta-modelling using the Fractal Dimension</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">52-67</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We discuss in this paper the development of a meta-model of an intelligent agent simulation model. Such intelligent agent models consist of a number of entities called agents, which interact with each other. The nature of such interactions creates emergent behaviour. In conflict or peacekeeping situations, these agents correspond to the actors in the situation (the different force elements, for example). A meta-model is thus a mathematical abstraction of such a simulation, composed of two parts. For the first part, the fractal dimension of a force is introduced as a parameter measuring the emergent ability of such forces to cluster locally, corresponding to local decision-making by individual agents. For the second part we consider the mathematics of Bayesian Decision-Making as a meta-model for top down decision processes in such simulation models.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Colin R. Mason</style></author><author><style face="normal" font="default" size="100%">James Moffat</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Decision Making Support: Representing the C2 Process in Simulations: Modelling the Human Decision-Maker</style></title><secondary-title><style face="normal" font="default" size="100%">2000 Winter Simulation Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10-13 Dec 2000</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Orlando, FL, USA</style></pub-location><pages><style face="normal" font="default" size="100%">940-949</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>