Case-Based Team Recognition Using Learned Opponent Models

Floyd, Michael W.; Karneeb, Justin; Aha, David W.

doi:10.1007/978-3-319-61030-6_9

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…We briefly summarize these efforts. Additional details can be found, for example, in Floyd, Karneeb, and Aha (2017) and Karneeb et al (2018).…”

Section: Oceanserveriver2mentioning

confidence: 99%

Goal Reasoning: Foundations, Emerging Applications, and Prospects

Aha¹

2018

AI Magazine

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Goal reasoning (GR) has a bright future as a foundation for the research and development of intelligent agents. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for some applications of deliberative autonomy. While studied in diverse AI sub-communities for multiple applications, our group has focused on how GR can play a key role for controlling autonomous systems. Thus, its importance is rapidly growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce GR, briefly relate it to other AI topics, summarize some of our group’s work on GR foundations and emerging applications, and describe some current and future research directions.

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“…We briefly summarize these efforts. Additional details can be found, for example, in Floyd, Karneeb, and Aha (2017) and Karneeb et al (2018).…”

Section: Oceanserveriver2mentioning

confidence: 99%

Goal Reasoning: Foundations, Emerging Applications, and Prospects

Aha¹

2018

AI Magazine

Self Cite

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“…), as well as the group formation. Floyd et al (2017) also use simulations to bootstrap a case-based reasoning system for identifying and classifying enemy combatants engaged in beyond-visual-range air combat. In some cases, generative models can become very elaborate, as with Shen and How (2019a), where Markov decision processes are specified for both adversarial and neutral agents, along with rationality and deception parameters.…”

Section: Goal-based Generative Modelsmentioning

confidence: 99%

A Survey of Opponent Modeling in Adversarial Domains

Nashed

Zilberstein

2022

jair

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Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

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“…That is to say, it is possible to extract the typical characteristics of aerial targets from the chaotic intelligence information, and then recognize their combat intentions. The existing research methods of targets intention recognition mainly include template matching method [4], expert system method [5]- [7], decision tree [8]- [9], Bayesian network [10]- [12] and neural network [13]- [15]. Floyd et al [4] applies the template matching method to combat intention recognition in beyond-visual-range air combat.…”

Section: Introductionmentioning

confidence: 99%

“…The existing research methods of targets intention recognition mainly include template matching method [4], expert system method [5]- [7], decision tree [8]- [9], Bayesian network [10]- [12] and neural network [13]- [15]. Floyd et al [4] applies the template matching method to combat intention recognition in beyond-visual-range air combat. Ben-Bassat and Freedy [5] evaluates military situation by integrating knowledge requirements and management into expert decision support systems.…”

Section: Introductionmentioning

confidence: 99%

Panoramic Convolutional Long Short-Term Memory Networks for Combat Intension Recognition of Aerial Targets

et al. 2020

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Aiming at the limitation that traditional methods for combat intention recognition of aerial targets are difficult to effectively capture the essential characteristics of intelligence information, we design a novel deep learning method, Panoramic Convolutional Long Short-Term Memory networks (PCLSTM), to improve the recognition ability. First, based on the characteristics of aerial target intelligence information, a panoramic convolutional layer is designed to extract the loosely coupled characteristics of intelligence information, and a time series pooling layer is designed to reduce the scale of neural network parameters on a large scale. Then, the temporal feature extraction capability of the LSTM layer and the depth feature mining capability of the traditional deep learning layer are combined to construct the PCLSTM neural network. Subsequently, the recognition performance of PCLSTM is analyzed by simulation experiments compared with standard deep net, convolutional neural network and LSTM network as benchmark models. Finally, PCLSTM was used to carry out simulation tests on different truncated data sets of original intelligence information, to analyze the optimal length of truncated data for different combat intention recognition. And then a reasonable aerial target combat intention recognition method is designed. The simulation results show that the method presented in this paper has theoretical significance and reference value for command decision-making. INDEX TERMS Aerial targets, combat intension recognition, deep learning, panoramic convolutional long short-term memory neural network.

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Case-Based Team Recognition Using Learned Opponent Models

Cited by 8 publications

References 4 publications

Goal Reasoning: Foundations, Emerging Applications, and Prospects

Goal Reasoning: Foundations, Emerging Applications, and Prospects

A Survey of Opponent Modeling in Adversarial Domains

Panoramic Convolutional Long Short-Term Memory Networks for Combat Intension Recognition of Aerial Targets

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