Large-Scale Air Combat Tactics Optimization Using Genetic Algorithms

Mulgund, Sandeep; Harper, Karen A.; Zacharias, Greg L.; Krishnakumar, Kalmanje

doi:10.2514/2.4689

Cited by 16 publications

(7 citation statements)

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“…Kelly reviews and summarizes much of this work [4], and specifically mentions metrics that include variables such as: relative aircraft position, throttle and speedbrake manipulation, and overall engagement outcomes to name a few. Identification of meaningful metrics that operate on time-series and summary data from engagements is still an active field of research [2,[4][5][6][7][8]. The work reported in this paper utilizes several metrics developed in the fighter pilot training literature as objective functions for automated learning and tuning of decision-making AI (although it is not exclusive to any particular set of metrics, or dog-fighting training applications, per se).…”

Section: A Problem Domain and Previous Workmentioning

confidence: 99%

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Adaptive Simulation-Based Training of Artificial-Intelligence Decision Makers Using Bayesian Optimization

Israelsen¹,

Ahmed²,

Green³

et al. 2018

Journal of Aerospace Information Systems

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This work studies how an AI-controlled dog-fighting agent with tunable decisionmaking parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima.These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multipoint Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.

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Section: A Problem Domain and Previous Workmentioning

confidence: 99%

“…Mulgund et al examined 'large-scale' air combat tactics (formations, etc.) and were able to demonstrate promising results in that area [5,7]. Wu et al addressed the problem of optimizing cooperative multiple target attack using genetic algorithms (GAs) [9].…”

Section: A Problem Domain and Previous Workmentioning

confidence: 99%

Adaptive Simulation-Based Training of Artificial-Intelligence Decision Makers Using Bayesian Optimization

Israelsen¹,

Ahmed²,

Green³

et al. 2018

Journal of Aerospace Information Systems

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“…Mulgund applied GA to optimize the team formation and intercept geometry in large-scale air combat 15 . Liang also used GA to design anti-torpedo tactics 16 .…”

Section: Related Workmentioning

confidence: 99%

Tactics Exploration Framework based on Genetic Programming

Yao¹,

Wang²,

Li³

et al. 2017

IJCIS

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Engagement-level simulation is a quantitative way to evaluate the effectiveness of weapon systems before construction and acquisition, minimizing the risk of investment. Though contractors have built simulation systems with high fidelity models of weapon systems and battlefields, developing competent tactics to give full play to new weapon systems in simulation experiments is labor intensive, as most classical tactics tend to be out of date. In this work, we proposed a tactics exploration framework (TEF) that applied grammar-based genetic programming (GP) to generating and evolving tactics in the engagementlevel simulation. Tactics are represented with modular behavior trees (BTs) for compatibility with the genetic operators. Experiments to explore submarine tactics have been conducted to observe and study the exploration process. The experimental results show that the TEF based on GP is efficient to explore tactics in the formalism of BTs.

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“…Moreover, these maneuvering strategies are fixed and limited for situational awareness, and it is difficult to completely cover all air combat situations. The other category is the self-learning method, including genetic algorithm [5] , artificial immune system [6] and reinforcement learning [7] , and so forth. These methods adopt their own experiences, and the models are optimized to cope with complex and changeable environments.…”

Section: Introductionmentioning

confidence: 99%

Research on autonomous maneuvering decision of UCAV based on approximate dynamic programming

Gao

Wang

2019

2019 International Conference on Image and Video Processing, and Artificial Intelligence

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Unmanned aircraft systems can perform some more dangerous and difficult missions than manned aircraft systems. In some highly complicated and changeable tasks, such as air combat, the maneuvering decision mechanism is required to sense the combat situation accurately and make the optimal strategy in real time. This paper presents a formulation of a 3-D one-on-one air combat maneuvering problem and an approximate dynamic programming approach for computing an optimal policy on autonomous maneuvering decision making. The aircraft learns combat strategies in a Reinforcement Leaning method, while sensing the environment, taking available maneuvering actions and getting feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to a rapidly changing tactical situation, learns in a long-term planning, without any explicitly coded air combat rule base.

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Large-Scale Air Combat Tactics Optimization Using Genetic Algorithms

Cited by 16 publications

References 1 publication

Adaptive Simulation-Based Training of Artificial-Intelligence Decision Makers Using Bayesian Optimization

Adaptive Simulation-Based Training of Artificial-Intelligence Decision Makers Using Bayesian Optimization

Tactics Exploration Framework based on Genetic Programming

Research on autonomous maneuvering decision of UCAV based on approximate dynamic programming

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