<?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%">Abbass, H.</style></author><author><style face="normal" font="default" size="100%">Bender, A.</style></author><author><style face="normal" font="default" size="100%">Gaidow, S.</style></author><author><style face="normal" font="default" size="100%">Whitbread, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Red Teaming: Past, Present and Future</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Intelligence Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Feb. 2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;background-color:rgb(255, 255, 255); color:rgb(51, 51, 51); font-family:arial,sans-serif&quot;&gt;The combination of Computational Intelligence (CI) techniques &amp;quot;with Multi-Agent Systems (MAS) offers a great deal of opportunities for practitioners and Artificial Intelligence (AI) researchers alike. CI techniques provide the means to search massive spaces quickly; find possible, better or optimum solutions in these spaces; construct algorithms, functions and strategies to control an autonomous entity; find patterns and relationships &amp;quot;within data, information, knowledge or experience; assess risk and identify strategies for risk treatment; and connect the dots to synthesize an overall situational awareness picture that decision makers can utilize. MAS provide the structured, modular, distributed and efficient software environment to simulate systems; the architecture to represent systems and entities naturally; the environment to allow entities to observe, communicate &amp;quot;with, negotiate &amp;quot;with, orient &amp;quot;with respect to, and act upon other entities; the modular representation that allows entities to store and manipulate observations, forming beliefs, desires, goals, plans, and intentions; and the framework to model behavior. By bringing CI and MAS together, we have a powerful computational environment that has the theoretical potential to do many things that one can expect &amp;quot;when attempting to structure, understand, and solve a problem. In this article, we follow two objectives. First, we &amp;quot;will present Computational Red Teaming (CRT) as the state-of-the-art architecture representing the integration of CI techniques and MAS for understanding competition. Second, we &amp;quot;will demonstrate how this integration of MAS and CI benefits practitioners in almost all major application domains by drawing examples from defense, business and engineering. We &amp;quot;will present the evolution of CRT by categorizing the different levels of integrating CI and MAS, and highlighting open research questions pertaining to CRT.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">30 - 42</style></section></record></records></xml>