<?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><authors><author><style face="normal" font="default" size="100%">Mikel Petty</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benefits and Consequences of Automated Learning  in Computer Generated Forces Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">automated learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer generated forces</style></keyword></keywords><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%">63-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Computer generated forces (CGF) are automated or semi-automated entities in a battlefield simulation that are generated and controlled by a computer system (the CGF system), perhaps assisted by a human operator. The idea that CGF systems can and should include automated learning capabilities has been widely asserted and accepted. However, it seems to be not so obvious that learning by CGF systems would necessarily be beneficial. For each of the three broad classes of CGF applications there are categories of learning-modified behavior for CGF systems that apparently could reduce or negate the utility of the CGF system for the application. Real and notional examples are available. The specific applications where learning by CGF systems might be useful are a subset of CGF applications.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">063</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frederic (Rick) McKenzie</style></author><author><style face="normal" font="default" size="100%">Mikel Petty</style></author><author><style face="normal" font="default" size="100%">Jean Catanzaro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Experimental Application of a Trait-Based Personality Model to the Simulation of Military Decision-Making</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer generated forces</style></keyword><keyword><style  face="normal" font="default" size="100%">personality modeling.</style></keyword></keywords><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%">75-92</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Personality is a significant influence on human behavior. In the context of military decision-making, different military commanders may behave differently when faced with the similar circumstances, depending on their personalities. Moreover, personality may cause the same commander to react differently to similar situations encountered at different times. The effect of personality on decision-making behavior is intrinsically complex and is further mediated by such factors as stress and situational context. This research investigates the inclusion of personality in models of military command decision-making. A simulation was implemented wherein a simulated commander must make critical decisions under multiple pressu-res. The commander’s human behavior model allows the specification of personality using a set of personality traits. In general, personality traits determine a person’s predisposition to exhibit a particular behavior under varying situational conditions. In this research, the commander’s personality traits and the situational conditions were combined to produce effects such as reaction time delay and decision accuracy and effectiveness modifications. The research showed that incorporating trait-based personality models of human behavior into simulations is feasible and can produce realistic effects on the decision-making of a simulated commander. It also revealed opportunities for further development of the approach.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">075</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mikel Petty</style></author><author><style face="normal" font="default" size="100%">Robert Franceschini</style></author><author><style face="normal" font="default" size="100%">Amar Mukherjee</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Terrain Reasoning Algorithm for Defending a Fire Zone</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%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1999</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">56-75</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Distributed simulation is an approach to building large-scale simulation models from a set of independent simulator nodes communicating via a network. The U.S. Army uses distributed simulation systems for both training and analysis. Those systems include both crewed simulators and computer generated forces (CGF) systems; the latter use software, rather than human crews, to generate the behavior of entities in the simulated battlefield. CGF systems must include algorithms for all of the tactical behaviors that are needed for the simulation. One such tactical behavior is “Fire Zone Defense”.  An algorithm for this behavior must select defensive deployment locations on the terrain for the individual entities (e.g., tanks) of a unit (e.g., a company) to effectively defend an assigned engagement area. The entities of the unit then move to those locations.
We developed a new algorithm for the behavior. It combines a geometric terrain analysis algorithm, which creates a weighted graph representation of the terrain, with a greedy optimization algorithm that operates on that graph. The algorithm was compared experimentally with a previously existing algorithm for the same behavior. The comparison used a metric that measured the cumulative observation of the engagement area from the selected locations of the defending entities. Under that metric the new algorithm consistently outperformed the existing algorithm, with an average ratio of performance over 2. The execution speeds for the two algorithms were approximately the same.</style></abstract></record></records></xml>