C3I systems analysis using the distributed interactive C3I effectiveness (DICE) simulation

Bowden, Fred D.; Gabrisch, Carsten; Davies, Mike

doi:10.1109/icsmc.1997.637393

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Gallagher et al [7] used dynamic programming method to evaluate the comprehensive effectiveness of weapon system. Bowden et al [8] constructed the effectiveness evaluation model of weapon system based on agent simulation technology. Kohlberg et al [9] constructed an efficiency cost ratio optimization model based on Lagrange operator for missile defense system.…”

Section: Rapid Development Stagementioning

confidence: 99%

Review on Effectiveness Evaluation of Missile Weapon System

Zhang¹,

Li²,

Zhao³

et al. 2021

IJFET

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In order to evaluate and compare the advantages and disadvantages of missile weapon systems and assist commanders in making optimal decisions, quantitative scales must be used to measure the effectiveness of missile weapon systems. This paper introduces the concept of missile weapon system effectiveness, expounds the importance and necessity of effectiveness evaluation, summarizes the relevant models, common methods and development process of effectiveness evaluation, analyzes the problems faced by missile weapon system effectiveness evaluation, and looks forward to the development trend of this research field.

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Section: Rapid Development Stagementioning

confidence: 99%

Review on Effectiveness Evaluation of Missile Weapon System

Zhang¹,

Li²,

Zhao³

et al. 2021

IJFET

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show abstract

“…Lessons learned and system specification have also been sparingly discussed [Balzer 1996;Stytz et al 1996]. As might be expected for a software system, architectural aspects of the CGAs have been addressed often and a wide variety of approaches were pursued [Adkins 1996;Becket and Badler 1993;Bergenthal 2000;Bokma and Slade 1993;Bowden et al 1997;Bowden and Davies 1998;Brasse et al 1999;Busetta et al 1999;Butler 1998;Calder et al 1996;Calder and Drummey 1999;Ceranowicz 1994;Courtemanche 1999;Courtemanche and Burch 2000;Cox and Flaglien 1999;Cusack and Hoare 1999;Franceschini et al 1999;Gagnon et al 1999;Ge et al 1995;Howard and Lee 1998;Jackson and Pearman 1999;Jones et al 1993;Kocabas and Oztemel 1998;Kuokka 1993;Labbiento 2000;Laird et al 1987;1995;LaVine et al 1999;Oztemel and Kocabas 1996;Parsons 1994;Patrone and Nardo 2000;Pratt et al 1994;Reece and Kelly 1996;Siksik 2000;Stytz and Banks 2000;Vrablik et al 1998;Webber and Badler 1993]. The architectural approaches range from modular software libraries and interacting processes to closely coupled objects and data-flow architectu...…”

Section: Introductionmentioning

confidence: 99%

The distributed mission training integrated threat environment system architecture and design

Stytz

Banks²

2001

ACM Trans. Model. Comput. Simul.

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We describe the architecture, design, components, and functionality of the Distributed Mission Training Integrated Threat Environment (DMTITE) software. The DMTITE architecture and design support the development and run-time operation of computer-generated actors (CGAs) in distributed simulations. The architecture and design employ object-oriented techniques, component software, object frameworks, containerization, and rapid prototyping technologies. The DMTITE architecture and design consist of highly modular components where interdependencies are well defined and minimized. DMTITE is an open architecture and open design, and most component and framework code is open source. The DMTITE architecture and design have been implemented (including all system components and frameworks) and currently support a number of types of computer-generated actors. The DMTITE architecture, design, and implementation are capable of supporting multiple reasoning, vehicle dynamics, skill level, and migration requirements for any type of CGA.

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“…The decision making process in command agents in present day military simulations, such as Warsim 2000 (McNett et al, 1997), and ModSAF (Ceranowicz, 1994a and) and JANUS (Pratt and Johnson, 1995) interfaced with decision support systems such as DICE (Bowden et al, 1997) and course of action generators such as Chapter 1 -Introduction 2 Fox-GA (Hayes et al, 1998) and CADET (Ground et al, 2002), is predominantly based on the military decision making process (MDMP) which in turn is based on the well known multi attribute utility analysis (MAUA) model. This decision making process is prescriptive; instructing how humans should take decisions.…”

Section: Introductionmentioning

confidence: 99%

Command Agents with Human-Like Decision Making Strategies

Ahmad

Gavrilov

Lee

et al. 2007

19th IEEE International Conference on Tools With Artificial Intelligence(ICTAI 2007)

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Human behaviour representation in military simulations is not sufficiently realistic, specially the decision making by synthetic military commanders. The decision making process lacks realistic representation of variability, flexibility, and adaptability exhibited by a single entity across various episodes. It is hypothesized that a widely accepted naturalistic decision model, suitable for military or other domains with high stakes, time stress, dynamic and uncertain environments, based on an equally tested cognitive architecture can address some of these deficiencies. And therefore, we have developed a computer implementation of Recognition Primed Decision Making (RPD) model using Soar cognitive architecture and it is referred to as RPD-Soar agent in this report. Due to the ability of the RPD-Soar agent to mentally simulate applicable courses of action it is possible for the agent to handle new situations very effectively using its prior knowledge. The proposed implementation is evaluated using prototypical scenarios arising in command decision making in tactical situations. These experiments are aimed at testing the RPD-Soar agent in recognising a situation in a changing context, changing its decision making strategy with experience, behavioural variability within and across individuals, and learning. The results clearly demonstrate the ability of the model to improve realism in representing human decision making behaviour by exhibiting the ability to recognise a situation in a changing context, handle new situations effectively, flexibility in the decision making process, variability within and across individuals, and adaptability. The observed variability in the implemented model is due to the ability of the agent to select a course of action from reasonable but some times sub-optimal choices available. RPD-Soar agent adapts by using 'chunking' process which is a form of explanation based learning provided by Soar architecture. The agent adapts to enhance its experience and thus improve its efficiency to represent expertise.

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C3I systems analysis using the distributed interactive C3I effectiveness (DICE) simulation

Cited by 4 publications

References 1 publication

Review on Effectiveness Evaluation of Missile Weapon System

Review on Effectiveness Evaluation of Missile Weapon System

The distributed mission training integrated threat environment system architecture and design

Command Agents with Human-Like Decision Making Strategies

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