arXiv subjects

Devaprakash Muniraj

Publications and source records attributed to Devaprakash Muniraj.

5 recordsLinked to original sources

arXiv2026

MR-TGN: A Meta-Role Temporal Graph Network for Team-Level Intent Prediction in Multi-Agent Systems

Collective intent prediction in multi-agent systems focuses on predicting the shared objectives and future behaviours of groups of interacting agents. The problem is particularly challenging because collective intent emerges from complex interactions, evolving cooperation structures, and long-term behavioural dependencies among heterogeneous agents operating in dynamic and partially observable environments. Furthermore, functional roles adopted by agents are often latent, may change over time, and are rarely available as explicit annotations, making the learning of coordinated group behaviours significantly more difficult. To address these challenges, this paper proposes a Meta-Role Temporal Graph Network (MR-TGN) framework for collective intent prediction in multi-agent systems. The proposed framework models agents as dynamically evolving graph entities and employs temporal memory mechanisms to encode historical interactions and coordination behaviours. To capture higher-level behavioural knowledge, MR-TGN introduces a memory-enhanced meta-role learning mechanism that derives latent role representations from agent-centric behavioral representations without requiring explicit role labels. An evaluation methodology for early collective intent prediction is proposed to assess prediction accuracy and timeliness, enabling realistic evaluation of the model's ability to anticipate collective objectives during the early stages of mission execution. Experimental results on representative multi-agent scenarios demonstrate that the proposed framework consistently outperforms competitive baselines and achieves effective early prediction of collective intents in dynamic and adversarial environments.

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arXiv2026

An Evidential Reasoning Approach for Aerial Target Classification and Intent Prediction

Timely classification and intent prediction of aerial targets is crucial for a combat aircraft to make informed tactical decisions. The prevailing approach for aerial target classification relies on data-driven models using time-series data. These models perform well with long-duration data; however, this is impractical in combat situations involving rapidly evolving threats that demand quick decisions. Minimizing false predictions is essential, as uncertainty is preferable to incorrect assessments in high-risk environments. Here, we propose an integrated approach to target classification and intent prediction that enables decisions from partial data in settings where threats require rapid response. In the proposed method, predictions are generated from short sequential sub-samples instead of the entire time series, and the results are refined by propagating beliefs across sub-samples. Outputs from classifiers are combined through an evidential reasoning framework to manage uncertainty. Target intent is inferred using rule-based techniques and a distance-based combination method to fuse information over time. Due to lack of publicly available datasets, a dataset for aerial target classification was generated for evaluation. A case study involving eight targets is used to demonstrate the effectiveness of the approach, whereby accuracies of 88% and 93% are achieved for target type classification and intent prediction, respectively.

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arXiv2026

Critical Infrastructure Defense Against Aerial Swarms Under Sensing Uncertainty: Online Allocation With Finite-Time Guarantees

This article presents a closed-loop, uncertainty-aware framework for defending a protected zone against coordinated incursions by swarms of small uncrewed aircraft systems (UAS). The interaction structure of the attackers is modeled as time-varying, while defenders operate under imperfect sensing. The proposed criticality-driven defender-to-attacker assignment strategy integrates three components: a probabilistic graph-based representation of the attacking swarm inferred from uncertain observations; a risk-aware attacker criticality model combining time-to-breach urgency with uncertainty; an online defender allocation mechanism that assigns and selectively reassigns defenders while limiting switching-induced instability through robust execution constraints. Analytical guarantees are established within a filtration-based first-hitting-time framework. In particular, finite-time triggering of the first capture event following detection is proven, and explicit mixed linear-geometric upper bounds are derived for the expected neutralization time. Monte Carlo simulations demonstrate the effectiveness of the proposed framework, achieving 85.6% neutralization efficiency under probabilistic sensing and 99.9% under deterministic sensing. Systematic ablation and sensitivity studies further quantify how detection thresholds and coordination parameters influence reliability and time-to-first-capture.

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arXiv2025

Adversarial Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Model Uncertainty

This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to uncertainties in the aerodynamic model of the sUAS. The controller is trained using the Robust Adversarial Reinforcement Learning framework, where an adversary perturbs the environment (aerodynamic model) to expose the agent (sUAS) to demanding scenarios. In our formulation, the adversary introduces rate-bounded perturbations to the aerodynamic model coefficients. We demonstrate that adversarial training improves robustness compared to controllers trained using stochastic model uncertainty. The learned controller is also benchmarked against a switched uncertain initial condition controller. The effectiveness of the approach is validated through high-fidelity simulations using a realistic six-degree-of-freedom fixed-wing aircraft model, showing accurate and robust path-following performance under a variety of uncertain aerodynamic conditions.

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arXiv2021

LFT Representation of a Class of Nonlinear Systems: A Data-Driven Approach

This paper focuses on developing a method to obtain an uncertain linear fractional transformation (LFT) system that adequately captures the dynamics of a nonlinear time-invariant system over some desired envelope. First, the nonlinear system is approximated as a polynomial nonlinear state-space (PNLSS) system, and a linear parameter-varying (LPV) representation of the PNLSS model is obtained. To reduce the potentially large number of scheduling parameters in the resulting LPV system, an approach based on the cascade feedforward neural network (CFNN) is proposed. We account for the approximation and reduction errors through the addition of a norm-bounded, causal, dynamic uncertainty in the LFT system. Falsification is used in a novel way to perform guided simulations for deriving a norm bound on the dynamic uncertainty and generating data for training the CFNN. Then, robustness analysis using integral quadratic constraint theory is carried out to choose an LFT representation that leads to a computationally tractable analysis problem and useful analysis results. Finally, the proposed approach is used to obtain an LFT representation for the nonlinear equations of motion of a fixed-wing unmanned aircraft system.

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