Multiagent-Based Approach for Risk Analysis in Mission Capability Planning

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<div class="col-main has-full-enumeration" id="kb-nav--main" style="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);"><div class="abstract-content formatted" style="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;"><p>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&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.</p></div></div><div class="col-aside" id="kb-nav--aside" style="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);"><div class="cover" style="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;"><div class="look-inside cover-image-animate" style="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;">&nbsp;</div></div></div>