Intelligent interaction model for battleship control based on the fusion of target intention and operator emotion

Wang, Xun; Lu, Aiguo; Liu, Jin; Kang, Zhiwei; Pan, Chao

doi:10.1016/j.compeleceng.2021.107196

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…Zhang et al [ 4 ] combined the advantages of deep learning and D-S evidence theory to develop an information fusion method for the intention recognition of multi-target formation in sea battlefield. Wang et al [ 32 ] proposed a warship human–machine intelligent interaction model based on the fusion of target intention and operator emotion. Some scholars have applied intelligent model-based air combat intention recognition methods to the battlefield.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Xia

Chen

Fang

2023

Entropy

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Battlefield information is generally incomplete, uncertain, or deceptive. To realize enemy intention recognition in an uncertain and incomplete air combat information environment, a novel intention recognition method is proposed. After repairing the missing state data of an enemy fighter, the gated recurrent unit (GRU) network, supplemented by the highest frequency method (HFM), is used to predict the future state of enemy fighter. An intention decision tree is constructed to extract the intention classification rules from the incomplete a priori knowledge, where the decision support degree of attributes is introduced to determine the node-splitting sequence according to the information entropy of partitioning (IEP). Subsequently, the enemy fighter intention is recognized based on the established intention decision tree and the predicted state data. Furthermore, a target maneuver tendency function is proposed to screen out the possible deceptive attack intention. The one-to-one air combat simulation shows that the proposed method has advantages in both accuracy and efficiency of state prediction and intention recognition, and is suitable for enemy fighter intention recognition in small air combat situations.

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Section: Literature Reviewmentioning

confidence: 99%

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Xia

Chen

Fang

2023

Entropy

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“…This can be done through a collection of technologies, such as wearable devices and sensors, as well as the fusion of machine intention recognition with the user emotion recognition. This helps provide the user/operator with a collaborative environment where they can be an active participant in the process [33] [34].…”

Section: Operator -Ai Interaction and Teamingmentioning

confidence: 99%

Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems

Doyle,

Tucci,

Zhu

et al. 2023

Preprint

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<p>The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature. As cross-discipline teams work together on complex machine learning challenges, finding a consensus of basic definitions in the literature is a more fundamental problem. As a step in the Delphi process to define issues with trust and barriers to the adoption of autonomous systems, our study first collected and ranked the top concerns from a panel of international experts from the fields of engineering, computer science, medicine, aerospace, and defence, with experience working with artificial intelligence. This document presents a summary of the literature definitions for nomenclature derived from expert feedback.</p>

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“…Redundant data should be eliminated through data cleaning, and more targeted decision data should be selected for coding to participate in the subsequent decision-making process. The research shows that the complexity of different enterprise cases is inconsistent [14][15][16][17], and the difficulty of decision-making is also different. Therefore, in the process of enterprise case auxiliary decision-making, it is necessary to determine the final decision-making problem, select more targeted decision-making materials to participate in decision-making, and improve the accuracy of decision-making results.…”

Section: Introductionmentioning

confidence: 99%

Multi Model Establishment of Enterprise Case Aided Decision-making Based on Data Analysis

Li,

Jiang,

et al. 2024

Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29,

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Conventional multiple models for enterprise case aided decision-making mainly use AHP (Analytic Hierarchy Process) analytic hierarchy process to measure the relative weight of multiple factors, which is vulnerable to the influence of normalized processing relationship, resulting in low accuracy of model classification. Therefore, a new multiple model for enterprise case aided decision-making needs to be designed based on data analysis. That is, data preprocessing is carried out in advance, the original data of the model is obtained through data cleaning, feature selection, and feature coding steps, and then model parameters are selected. Using data analysis technology, an enterprise case aided decision-making multiple model is established. The experimental results show that the classification accuracy of the designed enterprise case aided decision-making multiple model based on data analysis is high, which proves that the established enterprise case aided decision-making multiple model has good performance, reliability, and certain application value, and has made certain contributions to improving the effect of enterprise case aided decision-making.

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Intelligent interaction model for battleship control based on the fusion of target intention and operator emotion

Cited by 11 publications

References 12 publications

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems

Multi Model Establishment of Enterprise Case Aided Decision-making Based on Data Analysis

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