Select Publications
Preprints
2023, Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments, http://dx.doi.org/10.48550/arxiv.2301.10363
,2023, Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments, http://dx.doi.org/10.2139/ssrn.4373173
,2022, Contextually Aware Intelligent Control Agents for Heterogeneous Swarms, http://dx.doi.org/10.21203/rs.3.rs-2293295/v1
,2022, Contextually Aware Intelligent Control Agents for Heterogeneous Swarms, http://dx.doi.org/10.48550/arxiv.2211.12560
,2022, Lightweight Monocular Depth Estimation with an Edge Guided Network, http://dx.doi.org/10.48550/arxiv.2209.14829
,2022, Latent Preserving Generative Adversarial Network for Imbalance classification, http://dx.doi.org/10.48550/arxiv.2209.01555
,2022, Swarm Analytics: Designing Information Markers to Characterise Swarm Systems in Shepherding Contexts, http://dx.doi.org/10.1177/10597123221137090
,2022, Fusing Interpretable Knowledge of Neural Network Learning Agents For Swarm-Guidance, http://arxiv.org/abs/2204.00272v1
,2022, Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming, http://dx.doi.org/10.1109/ACCESS.2022.3180032
,2021, MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation, http://dx.doi.org/10.48550/arxiv.2111.12334
,2021, Towards Real-Time Monocular Depth Estimation for Robotics: A Survey, http://dx.doi.org/10.48550/arxiv.2111.08600
,2021, Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification, http://dx.doi.org/10.48550/arxiv.2111.04019
,2021, Does Adversarial Oversampling Help us?, http://dx.doi.org/10.48550/arxiv.2108.10697
,2021, Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces, http://dx.doi.org/10.48550/arxiv.2107.12706
,2020, Mixture of Spectral Generative Adversarial Networks for Imbalanced Hyperspectral Image Classification, http://dx.doi.org/10.48550/arxiv.2009.13037
,2020, Disturbances in Influence of a Shepherding Agent is More Impactful than Sensorial Noise During Swarm Guidance, http://dx.doi.org/10.48550/arxiv.2008.12708
,2020, Q-Learning with Differential Entropy of Q-Tables, http://dx.doi.org/10.48550/arxiv.2006.14795
,2020, Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding, http://dx.doi.org/10.48550/arxiv.2004.11543
,2020, Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Trees, http://dx.doi.org/10.48550/arxiv.2003.04675
,2020, Machine Education: Designing semantically ordered and ontologically guided modular neural networks, http://dx.doi.org/10.48550/arxiv.2002.03841
,2019, A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach, http://dx.doi.org/10.48550/arxiv.1912.07796
,2018, Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of Watchdog Artificial Intelligence Agents, http://dx.doi.org/10.48550/arxiv.1812.08960
,2018, Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction, http://dx.doi.org/10.48550/arxiv.1810.04344
,2018, Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey, http://dx.doi.org/10.48550/arxiv.1803.03093
,2018, The N-Player Trust Game and its Replicator Dynamics, http://dx.doi.org/10.48550/arxiv.1803.02443
,2018, Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines, http://dx.doi.org/10.48550/arxiv.1802.10203
,2018, Networking the Boids is More Robust Against Adversarial Learning, http://dx.doi.org/10.48550/arxiv.1802.10206
,2018, A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents, http://dx.doi.org/10.48550/arxiv.1802.09669
,2018, Computational Red Teaming in a Sudoku Solving Context: Neural Network Based Skill Representation and Acquisition, http://dx.doi.org/10.48550/arxiv.1802.09660
,2018, On the role of working memory in trading-off skills and situation awareness in Sudoku, http://dx.doi.org/10.48550/arxiv.1802.10079
,2018, Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-based Swarm Intelligence Model, http://dx.doi.org/10.48550/arxiv.1802.09647
,2017, Effects of update rules on networked N-player trust game dynamics, http://dx.doi.org/10.48550/arxiv.1712.06875
,2016, A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data, http://dx.doi.org/10.48550/arxiv.1604.00921
,2014, Visualizing Cognitive Moves for Assessing Information Perception Biases in Decision Making, http://dx.doi.org/10.48550/arxiv.1401.7193
,2009, Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem, http://dx.doi.org/10.48550/arxiv.0907.0597
,2009, Robustness and Adaptiveness Analysis of Future Fleets, http://dx.doi.org/10.48550/arxiv.0907.0598
,2009, Computational Scenario-based Capability Planning, http://dx.doi.org/10.48550/arxiv.0907.0520
,2009, Strategic Positioning in Tactical Scenario Planning, http://dx.doi.org/10.48550/arxiv.0907.0340
,A Temporal Ontology Guided Clustering Methodology with a Case Study on Detection and Tracking of Artificial Intelligence Topics, http://dx.doi.org/10.2139/ssrn.4200134
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