A Novel Resource Allocation for Anti-Jamming in Cognitive-UAVs: An Active Inference Approach

Being able to robustly interact with and navigate a dynamic environment has been a long-standing challenge in intelligent transportation systems. Autonomous agents can use models that mimic the human brain to learn how to respond to other participants’ actions in the environment and proactively coordinate with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. Active inference may be defined as the Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. It enables the agent to choose an action that leads to its preferred future observations. An exploring actionoriented model is introduced to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments. It is conducted by adapting active inference to an imitation learning approach and finding a theoretical connection between them. We present a multi-modal self-awareness architecture for autonomous driving systems where the proposed techniques are evaluated on their ability to model proper driving behavior. Experimental results provide the basis for the intelligent driving system to make more human-like decisions and improve agent performance to avoid a collision.

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“…where π(a k ) = P(•| Dk ) is a specific row in Γ and P( Ėa k ) is the GE's pdf related to the active states that can be calculated as [40]:…”

Section: Incremental Active Learning and Inferencementioning

confidence: 99%

Exploring Action-Oriented Models via Active Inference for Autonomous Vehicles

Nozari

Krayani

Marín

et al. 2023

Preprint

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“…UAVs can also be deployed as intermediaries between ground-based terminals, improving transmission link performance and enhancing reliability, security, coverage, and throughput [ 11 , 12 ]. As such, UAV-assisted communications are becoming increasingly vital in developing future wireless systems [ 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Krayani

Khan

Marchese

et al. 2023

Sensors

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Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

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“…UAVs can also be deployed as intermediaries between ground-based terminals, improving transmission link performance and enhancing reliability, security, coverage, and throughput [11,12]. As such, UAV-assisted communications are becoming increasingly vital in developing future wireless systems [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Goal-Directed Trajectory Planning using Active Inference in UAV-Assisted Wireless Networks

Krayani¹,

Khan²,

Marchese³

et al. 2023

Preprint

View full text Add to dashboard Cite

Deploying UAVs as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using TSPWP instances executed on various training examples. This dictionary contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

show abstract

A Novel Resource Allocation for Anti-Jamming in Cognitive-UAVs: An Active Inference Approach

Cited by 15 publications

References 9 publications

Exploring Action-Oriented Models via Active Inference for Autonomous Vehicles

Exploring Action-Oriented Models via Active Inference for Autonomous Vehicles

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

A Goal-Directed Trajectory Planning using Active Inference in UAV-Assisted Wireless Networks

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