FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action

Schlabach, J.; Hayes, Caroline C.; Goldberg, David E.

doi:10.1162/evco.1999.7.1.45

Cited by 57 publications

(17 citation statements)

References 5 publications

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“…[5]. DAISY ( Figure 1) to describe the development process followed to develop FOX [15]. Fox is a decision support tool to help battlestaff rapidly develop a wide variety of friendly courses of action.…”

Section: Communitybuilder Design Methodologymentioning

confidence: 99%

Community Builder: Structuring Agent Architectures to Facilitate Domain Tasks

Hayes¹

2000

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including tr gathering and maintaining the data needed, and completing and reviewing the collection of information. Send co collection of information, including suggestions for reducing this burden, to Washington Headquarterf Services, C Davis Highway, Suite 1204, Arlington, VA 22202-4302, and The results of this work include 1) Community Builder, a design methodology to assist software designers in designing mixed initiative, multi-agent intelligent decision support systems (DSSs), 2) Development of architectures for multi-agent decision support systems in several task domains and 3) Dyna-plan, a reusable framework describing the generic reasoning cycle used in most dynamic and uncertain environments. As the complexity of computing needs continually increases, multi-agent systems are becoming indispensable as approaches for making complex systems modular and manageable. However, designing effective organizations for multi-agent systems is far from simple. Each time an agent-based system is designed to support a new task, designing an effective architectural structure for the system of agents that works effectively for the task is difficult and time-consuming. The aim of the Community Builder and methodology is to provide software designers with a descriptive design methodology that will help them to identify the domain specific constraints on the agent architecture, and to organize the agents accordingly. The aim of the DynaPlan framework is to capture some of the structure common to many planning under uncertainty domains so that other developers can re-use some of the knowledge that we have gained in building related domains. AbstractThe results of this work include 1) Community Builder, a design methodology to assist software designers in designing mixed initiative, multi-agent intelligent decision support systems (DSSs), 2) Development of architectures for multi-agent decision support systems in several task domains and 3) Dyna-plan, a re-usable framework describing the generic reasoning cycle used in most dynamic and uncertain environments. As the complexity of computing needs continually increases, multi-agent systems are becoming indispensable as approaches for making complex systems modular and manageable. However, designing effective organizations for multi-agent systems is far from simple. Each time an agent-based system is designed to support a new task, designing an effective architectural structure for the system of agents that works effectively for the task is difficult and time-consuming. The aim of the CommunityBuilder methodology is to provide software designers with a descriptive design methodology that will help them to identify the domain specific constraints on the agent architecture, and to organize the agents accordingly. The aim of the DynaPlan framework is to capture some of the structure common to many planning under uncertainty domains so that other developers can re-use some of ...

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Section: Communitybuilder Design Methodologymentioning

confidence: 99%

Community Builder: Structuring Agent Architectures to Facilitate Domain Tasks

Hayes¹

2000

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“…We show a realization of a war-gaming scenario fashioned after FOX-GA (Schlabach, Hayes, & Goldberg, 1999), a genetic algorithm developed at the University of Illinois.…”

Section: Abatismentioning

confidence: 99%

“…Our approach to war gaming is based on COA generation and assessment concepts by Schlabach, Hayes, and Goldberg (1999). War gaming is the assessment of how well a specific friendly COA might perform in a battle against the enemy's COA (Kaiser & Proffitt, 1992).…”

Section: Battlespace Scenario Execution and War Gaming: A Modelbased mentioning

confidence: 99%

Soldier performance course of action (COA) visualization aids

Rozenblit¹,

Barnes²,

Momen³

et al. 2000

PsycEXTRA Dataset

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The computer revolution has resulted in extending the possibilities of battlespace visualization to the brigade commander and below. However, mobility and bandwidth considerations require that the systems be efficient to reflect the realities of modern combat. The Advanced Battlespace Architecture for Tactical Information Selection (ABATIS) is being developed to be a rapid planning and re-planning experimental environment. ABATIS's object-oriented architecture has the advantage of being able to rapidly construct a three-dimensional battlespace that will accurately represent the essential planning components of a brigade and smaller division battle environment. The basic architecture has been extended to include war-gaming logic as part of the software design, and examples are given that pertain to specific military problems. This capability will allow ABATIS to realize fully the implications of battlespace visualization by creating a human-computer synergy that encourages both human and machine to generate and evaluate possible courses of action and their consequences. The human performance implications are discussed, and particular attention is directed toward research issues related to terrain visualization, automation, decision making, and cognitive biases.

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“…When all persons go through respectively City 1, City 2 and City 3, the corresponding values of the makespans and costs in the additive case are (8, 800), (16,80) and (24, 8), whereas they are, in the max case, (8, 100), (16,10) and (24, 1).…”

Section: A Multi-objective Problem Problem Descriptionmentioning

confidence: 99%

“…Some approaches use a specific representation (e.g. dedicated to the battlefield courses of action [16]). Most of the domain-independent approaches see a plan as a program and rely on Genetic Programming and on the traditional blocks-world domain for experimentation (starting with the Genetic Planner [18]).…”

Section: Introductionmentioning

confidence: 99%

Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms

Schoenauer¹,

Savéant

Vidal³

Natural Computing Series

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Summary. An original approach, termed Divide-and-Evolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solutions, and thus fail when the local method stops working on the complete problem, the Divideand-Evolve approach splits the problem at hand into several, hopefully easier, subproblems, and can thus solve globally problems that are intractable when directly fed into deterministic OR algorithms. But the most prominent advantage of the Divide-and-Evolve approach is that it immediately opens up an avenue for multiobjective optimization, even though the OR method that is used is single-objective. Proof of concept approach on the standard (single-objective) Zeno transportation benchmark is given, and a small original multi-objective benchmark is proposed in the same Zeno framework to assess the multi-objective capabilities of the proposed methodology, a breakthrough in Temporal Planning.

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FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action

Cited by 57 publications

References 5 publications

Community Builder: Structuring Agent Architectures to Facilitate Domain Tasks

Community Builder: Structuring Agent Architectures to Facilitate Domain Tasks

Soldier performance course of action (COA) visualization aids

Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms

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