<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Evolving High Fidelity Low Complexity Sheepdog Herding Simulations Using a Machine Learner Fitness Function Surrogate for Human Judgement</style></title><secondary-title><style face="normal" font="default" size="100%">AI 2015: Advances in Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><number><style face="normal" font="default" size="100%">9457</style></number><edition><style face="normal" font="default" size="100%"> Bernhard Pfahringer, Jochen Renz </style></edition><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Switzerland</style></pub-location><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>10</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Employing ABD Technology for Wargaming Courses Of Action</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div id=&quot;abstract&quot; style=&quot;margin: 0px 0px 16px; padding: 0px; border: 0px; font-weight: normal; font-style: normal; font-size: 12px; font-family: arial, sans-serif; vertical-align: baseline; color: rgb(0, 0, 0); font-variant: normal; letter-spacing: normal; line-height: 16.799999237060547px; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;&quot;&gt;&lt;p&gt;Abstract. The paper describes a system, known as TDSS, constructed to allow the wargaming of Courses Of Action (COA). TDSS employs agent-based distillation (ABD) technology, being built atop the CROCADILE simulation engine, representing battlefield assets as independent entities in a 3D environment. An experimental framework involving twenty five (25) Australian Army junior commanders, in the same manner that Tactical Exercises Without Troops (TEWTs) are assessed within the Australian Army, was developed and used to test the new tool against traditional means of wargaming. COAs were wargamed using the traditional approach as well as employing TDSS. User and Instructing officer evaluations were elicited, contrasting the two approaches. The results from the experimentation show that a clear and significant increase in military insight and understanding is gained through the use of the new tool. Similarly there was a significant increase in the confidence in results obtained through the use of the new tool, primarily as a result of the ability to data-farm a course of action as well as to clearly visualise the flow of action. In addition, there was a significant reduction in the time taken to conduct the wargame. 1.&lt;/p&gt;&lt;/div&gt;&lt;div id=&quot;citations&quot; style=&quot;margin: 0px; padding: 0px; border: 0px; font-weight: normal; font-style: normal; font-size: 12px; font-family: arial, sans-serif; vertical-align: baseline; color: rgb(0, 0, 0); font-variant: normal; letter-spacing: normal; line-height: 16.799999237060547px; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;&quot;&gt;&amp;nbsp;&lt;/div&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lam T. Bui</style></author><author><style face="normal" font="default" size="100%">Axel Bender</style></author><author><style face="normal" font="default" size="100%">Michael Barlow</style></author><author><style face="normal" font="default" size="100%">Hussein A. Abbass</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiagent-Based Approach for Risk Analysis in Mission Capability Planning</style></title><secondary-title><style face="normal" font="default" size="100%">Agent-Based Evolutionary Search</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin </style></pub-location><volume><style face="normal" font="default" size="100%">pp  77-96</style></volume><isbn><style face="normal" font="default" size="100%">978-3-642-13425-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;col-main has-full-enumeration&quot; id=&quot;kb-nav--main&quot; style=&quot;border: 0px; font-family: 'Helvetica Neue', Arial, Helvetica, sans-serif; font-size: 13px; font-style: normal; font-variant: normal; font-weight: normal; line-height: 13px; margin: 0px 0px 0px 40px; padding: 0px; vertical-align: baseline; outline: 0px; display: inline; width: 580px; float: left; position: relative; color: rgb(51, 51, 51); letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255);&quot;&gt;&lt;div class=&quot;abstract-content formatted&quot; style=&quot;border: 0px; font-family: inherit; font-size: inherit; font-style: inherit; font-variant: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline; outline: 0px; display: block;&quot;&gt;&lt;p&gt;In this chapter, we propose a multiagent-based approach for risk analysis in military capability planning. A hierarchical system is introduced that has two layers: an Option Production Layer (OPL) to find all possible options for the given planning problem, and a Risk Tolerance Layer (RTL) in which DMs&amp;rsquo; acceptance of risk is evolved. The OPL uses metaheuristic techniques such as evolutionary algorithms to deal with multi-objectivity of a class of NP-hard resource investment problems, called the Mission Capability Planning Problem (MCPP), under the presence of risk factors. This problem has at least two inherent conflicting objectives: minimizing the cost of investment in resources as well as optimizing the makespan of plans. The framework allows for the addition of a risk-based objective to the problem in order to support risk assessment during the planning process. The RTL is run by a multi-agent system which simulates the risk attitudes of DM. The system determines different types of attitudes towards risk with each type applying to a sub-set of MCPP solutions. The goal of each agent is to maximize its risk tolerance levels with respect to a given subset of solutions determined in the OPL. Risk tolerance levels are used as surrogates for risk attitudes. The hierarchical system is flexible in terms of using a feedback mechanism when necessary. The RTL uses information from the OPL and can itself return some hyper-information to guide the OPL further. In a case study, we use a mission planning scenario to validate our proposal. The results from this study demonstrate the advantage of our proposed system. A diverse set of agents was found; hence different types of options can be grouped and offered to the decision-makers.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-aside&quot; id=&quot;kb-nav--aside&quot; style=&quot;border: 0px; font-family: 'Helvetica Neue', Arial, Helvetica, sans-serif; font-size: 13px; font-style: normal; font-variant: normal; font-weight: normal; line-height: 13px; margin: 0px 0px 0px 40px; padding: 0px; vertical-align: baseline; outline: 0px; display: inline; width: 240px; float: left; color: rgb(51, 51, 51); letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255);&quot;&gt;&lt;div class=&quot;cover&quot; style=&quot;border: 0px; font-family: inherit; font-size: inherit; font-style: inherit; font-variant: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline; outline: 0px; display: block;&quot;&gt;&lt;div class=&quot;look-inside cover-image-animate&quot; style=&quot;border: 0px; font-family: inherit; font-size: inherit; font-style: inherit; font-variant: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline; outline: 0px; display: block; min-height: 188px; position: relative; max-width: 170px; text-decoration: none;&quot;&gt;&amp;nbsp;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</style></abstract><section><style face="normal" font="default" size="100%">Adaptation, Learning, and Optimization</style></section></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%">CROCADILE - An Open, Extensible Agent-Based Distillation Engine</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%">17-51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper describes a new multi-agent based distillation system known as CROCADILE – Conceptual Research Oriented Combat Agent Distillation Implemented in the Littoral Environment. CROCADILE shares the common features of other distillations such as MANA or EINSTein – an abstracted representation of the capabilities and behaviour of the conflict participants, from the interactions of those participants combat behaviour is seen to emerge. CROCADILE extends the realm of distillation systems in a number of novel and powerful ways delivering an open, extensible distillation engine realised in a 3D world and with variable levels of fidelity. In particular some of the key aspects that differentiate CROCADILE are: a 3D or 2D simulation engine; projectile or probabilistic hit resolution; units that can move by land, sea or air; support for multiple agent behaviour paradigms and user written agents; a high fidelity combat model that incorporates indirect fire, line of sight, round penetration and burst effects; sophisticated configurable command, mission, communication, and team structures; comprehensive logging of simulation results; and a database system for saving and loading world objects. CROCADILE follows an object-oriented approach in its design and realisation at all levels, making it easily extensible. Written in the Java language it is platform independent and freely available. The paper first covers the design philosophy and implementation of CROCADILE. A scenario concerning a potential future force structure for the Australian Army is then employed as a means of illustrating some of the features of the system. A number of runs of the system for the given scenario are analysed as to outcome for the teams of agents, weapon effectiveness, and the distribution of hits, damage and agent deaths within the physical combat space.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>