ORCID as entered in ROS

Select Publications
2021, Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces, http://arxiv.org/abs/2107.12706v2
,2020, Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures, http://dx.doi.org/10.36227/techrxiv.12834002.v2
,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/10.1109/MCI.2020.2998307
,2020, Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution, http://arxiv.org/abs/2008.12639v1
,2020, Q-Learning with Differential Entropy of Q-Tables, http://arxiv.org/abs/2006.14795v1
,2020, Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding, http://arxiv.org/abs/2004.11543v4
,2020, Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Trees, http://arxiv.org/abs/2003.04675v4
,2020, Machine Education: Designing semantically ordered and ontologically guided modular neural networks, http://arxiv.org/abs/2002.03841v1
,2019, A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach, http://arxiv.org/abs/1912.07796v4
,2019, Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding, http://arxiv.org/abs/1901.00949v1
,2019, Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design, http://arxiv.org/abs/1903.09297v1
,2018, Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of Watchdog Artificial Intelligence Agents, http://arxiv.org/abs/1812.08960v1
,2018, Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction, http://arxiv.org/abs/1810.04344v1
,2018, Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges, http://arxiv.org/abs/1808.06211v1
,2018, Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey, http://arxiv.org/abs/1803.03093v1
,2018, The N-Player Trust Game and its Replicator Dynamics, http://dx.doi.org/10.1109/TEVC.2015.2484840
,2018, Networking the Boids is More Robust Against Adversarial Learning, http://arxiv.org/abs/1802.10206v1
,2018, A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents, http://dx.doi.org/10.1016/j.knosys.2016.02.012
,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, Computational Red Teaming in a Sudoku Solving Context: Neural Network Based Skill Representation and Acquisition, http://dx.doi.org/10.1007/978-3-319-27000-5_26
,2018, On the role of working memory in trading-off skills and situation awareness in Sudoku, http://dx.doi.org/10.1007/978-3-319-12643-2_69
,2018, Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-based Swarm Intelligence Model, http://arxiv.org/abs/1802.09647v1
,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/10.1109/ACCESS.2016.2571058
,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://arxiv.org/abs/0907.0598v1
,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
,2005, Oiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non--Stationary Environments, http://arxiv.org/abs/cs/0502021v1
,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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