Distributed Discrepancy Detection for a Goal Reasoning Agent in Beyond-Visual-range Air Combat
AI Communications(2018)SCI 4区
Knexus Res Corp | US Navy
Abstract
We describe an extension of the Tactical Battle Manager , which uses goal reasoning techniques to control unmanned air vehicles in simulated scenarios of beyond-visual-range air combat. Our prior work with the Tactical Battle Manager focused primarily on behavior recognition, the task of identifying the behaviors being performed by hostile aircraft. In this article, we instead focus on distributed discrepancy detection and response. We also describe an ablation study for which we report evidence that these discrepancy management components improve mission success.
MoreTranslated text
Key words
Goal reasoning,discrepancy detection,air combat
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
IEEE Access 2019
被引用39
Defence Technology 2022
被引用13
Weapon Target Assignment Method for Multiple UAVs in Beyond-Visual-Range Air Combat
Chinese Journal of Management Science 2022
被引用2
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest