DRAGON-V: Detection and Recognition of Airplane Goals with Navigational Visualization

Wayllace, Christabel; Ha, Sunwoo; Han, Yuchen; Hu, Jiaming; Monadjemi, Shayan; Yeoh, William; Ottley, Alvitta

doi:10.1609/aaai.v34i09.7108

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Goal recognition is also related to the problem of Plan Recognition [128], which consists of trying to infer the actual plan adopted by the observed agent. The task of goal recognition has a number of potential and actual applications, including assisting the handicapped [129], activities of daily living [130], workplace safety [131], among others [132,133].…”

Section: Goal and Plan Recognitionmentioning

confidence: 99%

Resilience, reliability, and coordination in autonomous multi-agent systems

Cardoso

Logan

Meneguzzi

et al. 2022

AIC

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Multi-agent systems is an evolving discipline that encompasses many different branches of research. The long-standing Agents at Aberdeen ( A 3 ) group undertakes research across several areas of multi-agent systems, focusing in particular on aspects related to resilience, reliability, and coordination. In this article we introduce the group and highlight past research successes in those themes, building a picture of the strengths within the group. We close the paper outlining the future direction of the group and identify key open challenges and our vision towards solving them.

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Section: Goal and Plan Recognitionmentioning

confidence: 99%

Resilience, reliability, and coordination in autonomous multi-agent systems

Cardoso

Logan

Meneguzzi

et al. 2022

AIC

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“…The X-Plane Connect (XPC) Toolbox is an open source research tool used to interact with the X-Plane commercial flight simulator and allows users to control an aircraft and receive state information from X-Plane by communicating hundreds of flight data in real time. This research tool has been used to visualize flight paths, test control algorithms, and simulate an active airspace with various aircraft and airlines [ 35 , 36 ].…”

Section: Technical Backgroundmentioning

confidence: 99%

Learning to Have a Civil Aircraft Take Off under Crosswind Conditions by Reinforcement Learning with Multimodal Data and Preprocessing Data

Liu

Dai

Zhao

2021

Sensors

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Autopilot technology in the field of aviation has developed over many years. However, it is difficult for an autopilot system to autonomously operate a civil aircraft under bad weather conditions. In this paper, we present a reinforcement learning (RL) algorithm using multimodal data and preprocessing data to have a civil aircraft take off autonomously under crosswind conditions. The multimodal data include the common flight status and visual information. The preprocessing is a new design that maps some flight data by nonlinear functions based on the general flight dynamics before these data are fed into the RL model. Extensive experiments under different crosswind conditions with a professional flight simulator demonstrate that the proposed method can effectively control a civil aircraft to take off under various crosswind conditions and achieve better performance than trials without visual information or preprocessing data.

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“…Its aim is to infer the top-level goal of an agent based on input observation sequences and output an explanation of the observation sequence [1]. Currently, goal recognition has been applied in various fields, including intelligent assistants [2,3], autonomous driving [4,5], robot navigation [6,7], military confrontation [8], and more [9]. For example, in the application of goal recognition in a smart home assistant [2,3], the input observation sequence consists of a series of actions performed by a person as observed by a camera.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Online Goal Recognition in Continuous Domains via Deep Reinforcement Learning

Fang,

Chen,

Zeng

et al. 2023

Entropy

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The problem of goal recognition involves inferring the high-level task goals of an agent based on observations of its behavior in an environment. Current methods for achieving this task rely on offline comparison inference of observed behavior in discrete environments, which presents several challenges. First, accurately modeling the behavior of the observed agent requires significant computational resources. Second, continuous simulation environments cannot be accurately recognized using existing methods. Finally, real-time computing power is required to infer the likelihood of each potential goal. In this paper, we propose an advanced and efficient real-time online goal recognition algorithm based on deep reinforcement learning in continuous domains. By leveraging the offline modeling of the observed agent’s behavior with deep reinforcement learning, our algorithm achieves real-time goal recognition. We evaluate the algorithm’s online goal recognition accuracy and stability in continuous simulation environments under communication constraints.

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DRAGON-V: Detection and Recognition of Airplane Goals with Navigational Visualization

Cited by 4 publications

References 2 publications

Resilience, reliability, and coordination in autonomous multi-agent systems

Resilience, reliability, and coordination in autonomous multi-agent systems

Learning to Have a Civil Aircraft Take Off under Crosswind Conditions by Reinforcement Learning with Multimodal Data and Preprocessing Data

Real-Time Online Goal Recognition in Continuous Domains via Deep Reinforcement Learning

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