Preparing for the aftermath: Using emotional agents in game-based training for disaster response

Djordjevich, Donna D.; Xavier, Patrick G.; Bernard, Michael L.; Whetzel, Jon; Glickman, Matthew R.; Verzi, Stephen Joseph

doi:10.1109/cig.2008.5035649

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Traditional architectures such as finite state machines (FSMs) and behavior trees provide intuitive structures for designing agents, yet have difficulties in scaling the agents to handle more tasks and are too predictable in their behavior execution. Cognitive modeling solutions such as SOAR (Laird, 2012), ACT-R (Anderson & Lebiere, 1998), and Sandia's Cognitive Runtime Engine with Active Memory (SCREAM) (Djordjevich, et al, 2008), provided new architectures mimicking psychological models of human-decision making, with demonstrations of these models used as agents within various games (Laird, 2001) (Best, Lebiere, & Scarpinatto, 2002). These approaches have not met wide acceptance due to their complicated structure for authoring new behaviors.…”

Section: Related Workmentioning

confidence: 99%

Dante Agent Architecture for Force-On-Force Wargame Simulation and Training

Hart

Gayle

et al. 2021

AIIDE

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Physical site security heavily relies on expert teams continually examining and testing security profiles for discovering potential vulnerabilities. These experts hypothesize scenario(s) of interest and conduct “red versus blue” simulated exercises where they execute tactics that might reveal possible dangers. Due to the intensive manpower required, video-game environments have become a widely-adopted mechanism for conducting these exercises with virtual agents replacing many of the human roles for quicker analyses. However, these agents either have limited capabilities or require several engineers to develop realistic behaviors. This paper documents an agent architecture and authoring suite that enables subject matter experts to easily build complex attack/response plans for agents to use within Dante, a 3D simulation platform for video-game-based training/analysis of force-on-force engagements. This work expands upon current trends in commercial video-game artificial intelligence (AI) architectures to build agent behaviors deemed qualitatively valid by security experts, with the runtime of these algorithms best suited for turn-based, strategy games.

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Section: Related Workmentioning

confidence: 99%

Dante Agent Architecture for Force-On-Force Wargame Simulation and Training

Hart

Gayle

et al. 2021

AIIDE

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“…And these games and simulations need content. Militaries need scenarios to train peace-keeping duties and simulate the consequences of tactical decisions [25]; rescue services need city layouts and buildings to train disaster relief workers [26]; companies in sectors from logistics to customer service to education use game-based simulations to train their employees, and need scenarios for this. For training to be effective, desired affective states of the trainees need to be reliably induced, reinforcing the need for basing the content generation on a model of the trainee's experience profile.…”

Section: Procedural Content Generationmentioning

confidence: 99%

Experience-Driven Procedural Content Generation

Yannakakis

Togelius

2011

IEEE Trans. Affective Comput.

353

315

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Abstract-Procedural content generation (PCG) is an increasingly important area of technology within modern human-computer interaction (HCI) design. Personalization of user experience via affective and cognitive modeling, coupled with real-time adjustment of the content according to user needs and preferences are important steps towards effective and meaningful PCG. Games, Web 2.0, interface and software design are amongst the most popular applications of automated content generation. The paper provides a taxonomy of PCG algorithms and introduces a framework for PCG driven by computational models of user experience. This approach, which we call Experience-Driven Procedural Content Generation (EDPCG), is generic and applicable to various subareas of HCI. We employ games as an indicative example of rich HCI and complex affect elicitation, and demonstrate the approach's effectiveness via dissimilar successful studies.

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“…The 2006-2008 LDRD, "Enabling Immersive Simulation", produced several integrations, including: Trainable Automated Forces and Cognitive Foundry cognitive models with Umbra; and NPS's Delta3D with ABL [Abbott, et al, 2009]. Another LDRD project integrated SCREAM+SHERCA-based cognitive modeling elements into the behaviors of non-player-characters (NPCs) in a serious game for training first response commanders [Djordjevich, et al, 2008]. Results of these projects supported the observation that while it may be scientifically important to expand the domains and levels of abstraction that a cognitive framework can cover, it would be also important to pursue behavior modeling that applies the most capable techniques for different aspects of the problem and enables their integration.…”

Section: Sandia Embodied Agent Simulation Technologymentioning

confidence: 99%

LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC.

Djordjevich¹,

Xavier²,

Brannon³

et al. 2012

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This LDRD sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed pathplanning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables "one-click" layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimental study to determine where parallelization would be productive in Umbra-based FOF simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication. 4 ACKNOWLEDGMENTSThe authors would like to thank the following individuals for their contributions to the project:

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Preparing for the aftermath: Using emotional agents in game-based training for disaster response

Cited by 11 publications

References 16 publications

Dante Agent Architecture for Force-On-Force Wargame Simulation and Training

Dante Agent Architecture for Force-On-Force Wargame Simulation and Training

Experience-Driven Procedural Content Generation

LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC.

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