On games and simulators as a platform for development of artificial intelligence for command and control

Goecks, Vinicius G.; Waytowich, Nicholas R.; Asher, Derrik E.; Park, Song Jun; Mittrick, Mark R.; Richardson, John T. E.; Vindiola, Manuel M.; Logie, Anne; Dennison, Mark; Trout, Theron; Narayanan, Priya; Kott, Alexander

doi:10.1177/15485129221083278

Cited by 16 publications

(14 citation statements)

References 29 publications

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“…Goecks et al discuss the past and current efforts on how games and simulators, together with the AI algorithms, have been adapted to simulate certain aspects of military missions and how they might impact the future battleőeld. Moreover, they investigate how advances in virtual reality (VR) and visual augmentation (VA) systems provide new frontiers in human interfaces with gaming platforms and their military [41].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Wargaming Approach to Increase Course of Action Effectiveness in Military Operations

Candan

Guner

Yüksek

et al. 2023

AIAA SCITECH 2023 Forum

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In this study, an intelligent wargaming approach is proposed to evaluate the effectiveness of a military operation plan in terms of operational success and survivability of the assets. The proposed application is developed based on classical military decision making and planning (MDMP) workflow for ease of implementation into the real-world applications. Contributions of this study are threefold; a) developing an intelligent wargaming approach to accelerate the course of action (COA) analysis step in the MDMP which leads creating more candidate COAs for a military operation, b) generating effective tactics against the opposite forces to increase operational success, and c) developing an efficient, visual wargame-based MDMP framework for future systems that require a small team of operators to supervise a network of automated agents. Several example engagement scenarios are performed to evaluate the system capabilities and results are given. Moreover, fleet composition issue for automated agents is investigated and the fleet composer algorithm with hyperparameter tuning architecture is proposed.

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Section: Related Workmentioning

confidence: 99%

Intelligent Wargaming Approach to Increase Course of Action Effectiveness in Military Operations

Candan

Guner

Yüksek

et al. 2023

AIAA SCITECH 2023 Forum

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“…11 Multiagent simulations with imperfect information are especially suitable for analyzing military engagements. 12…”

Section: Agent-based Modeling and Simulationmentioning

confidence: 99%

“…For example, agent‐based models have been used in the healthcare, transportation, warfare, and disaster response domains 11 . Multiagent simulations with imperfect information are especially suitable for analyzing military engagements 12 …”

Section: Introductionmentioning

confidence: 99%

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

Humann

Fletcher

Gerdes

2023

Systems Engineering

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As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.

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“…In this paper, we utilize a custom made StarCraft II map that we developed in a previous paper 10,11 called "TigerClaw". TigerClaw is a tactical command and control scenario where Blue Force units must engage and eliminate the Red Force units that currently occupy a neutral city.…”

Section: Starcraft II Environmentmentioning

confidence: 99%

Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II

Waytowich¹,

Hare²,

Goecks³

et al. 2022

Preprint

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Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating. However, in real-world scenarios, expert data is limited and it is desired to train an agent that learns a behavior policy general enough to handle situations that were not demonstrated by the human expert. Another alternative is to learn these policies with no supervision via deep reinforcement learning, however, these algorithms require a large amount of computing time to perform well on complex tasks with high-dimensional state and action spaces, such as those found in StarCraft II. Automatic curriculum learning is a recent mechanism comprised of techniques designed to speed up deep reinforcement learning by adjusting the difficulty of the current task to be solved according to the agent's current capabilities. Designing a proper curriculum, however, can be challenging for sufficiently complex tasks, and thus we leverage human demonstrations as a way to guide agent exploration during training. In this work, we aim to train deep reinforcement learning agents that can command multiple heterogeneous actors where starting positions and overall difficulty of the task are controlled by an automatically-generated curriculum from a single human demonstration. Our results show that an agent trained via automated curriculum learning can outperform stateof-the-art deep reinforcement learning baselines and match the performance of the human expert in a simulated command and control task in StarCraft II modeled over a real military scenario.

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On games and simulators as a platform for development of artificial intelligence for command and control

Cited by 16 publications

References 29 publications

Intelligent Wargaming Approach to Increase Course of Action Effectiveness in Military Operations

Intelligent Wargaming Approach to Increase Course of Action Effectiveness in Military Operations

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II

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