Hussein Abbass

BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs

Publisher: Elsevier BV

Date: 09-2020

DOI: 10.1016/J.PATCOG.2020.107381

Automatic Generation of Controllers for Embodied Legged Organisms: A Pareto Evolutionary Multi-Objective Approach

Publisher: MIT Press - Journals

Date: 09-2004

DOI: 10.1162/1063656041774974

Abstract: In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic ersity was being maintained naturally by virtue of the system's inherent multi-objectivity.

Program evolution with explicit learning

Publisher: IEEE

Date: 2003

DOI: 10.1109/CEC.2003.1299869

An information-theoretic landscape analysis of neuro-controlled embodied organisms

Publisher: Springer Science and Business Media LLC

Date: 04-2004

DOI: 10.1007/S00521-003-0395-7

DIFFERENTIAL EVOLUTION FOR SOLVING MULTIOBJECTIVE OPTIMIZATION PROBLEMS

Publisher: World Scientific Pub Co Pte Lt

Date: 06-2004

DOI: 10.1142/S0217595904000217

Abstract: The use of evolutionary strategies (ESs) to solve problems with multiple objectives [known as vector optimization problems (VOPs)] has attracted much attention recently. Being population-based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the "strength Pareto evolutionary algorithm", one of the state-of-the-art evolutionary algorithm for solving VOPs.

Co-Operative Coevolutionary Neural Networks for Mining Functional Association Rules

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 06-2017

DOI: 10.1109/TNNLS.2016.2536104

The First IEEE Symposium on Artificial Life, IEEE-ALife'07, April 1-5, 2007, Honolulu, Hawaii, USA

Publisher: IEEE

Date: 04-2007

DOI: 10.1109/ALIFE.2007.367649

Developing Attention Focus Metrics for Autonomous Hypothesis Generation in Data Mining

Publisher: Springer Berlin Heidelberg

Date: 2012

DOI: 10.1007/978-3-642-34859-4_29

The reliability and transparency bases of trust in human-swarm interaction: principles and implications

Publisher: Informa UK Limited

Date: 13-05-2020

DOI: 10.1080/00140139.2020.1764112

Applications of information systems to homeland security and defense

Publisher: IGI Global

Date: 2005

DOI: 10.4018/978-1-59140-640-2

Connectivity-Aware Particle Swarm Optimisation for Swarm Shepherding

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 06-2023

DOI: 10.1109/TETCI.2022.3195178

Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/ACCESS.2022.3180032

Elucidating the benefits of a self-adaptive pareto EMO approach for evolving legged locomotion in artificial creatures

Publisher: IEEE

Date: 2003

DOI: 10.1109/CEC.2003.1299743

Preface

Publisher: IEEE

Date: 2006

DOI: 10.1109/CEC.2003.1299547

Separated and overlapping community detection in complex networks using multiobjective Evolutionary Algorithms

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/CEC.2010.5586522

A multi-objective risk-based approach for airlift task scheduling using stochastic bin packing

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/CEC.2010.5586005

Multi Objective Learning Classifier Systems Based Hyperheuristics for Modularised Fleet Mix Problem

Publisher: Springer Berlin Heidelberg

Date: 2012

DOI: 10.1007/978-3-642-34859-4_38

A survey of probabilistic model building genetic programming

Publisher: Springer Berlin Heidelberg

Date: 2007

DOI: 10.1007/978-3-540-34954-9_6

Characterizing warfare in red teaming

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 04-2006

DOI: 10.1109/TSMCB.2005.855569

Abstract: Red teaming is the process of studying a problem by anticipating adversary behaviors. When done in simulations, the behavior space is ided into two groups one controlled by the red team which represents the set of adversary behaviors or bad guys, while the other is controlled by the blue team which represents the set of defenders or good guys. Through red teaming, analysts can learn about the future by forward prediction of scenarios. More recently, defense has been looking at evolutionary computation methods in red teaming. The fitness function in these systems is highly stochastic, where a single configuration can result in multiple different outcomes. Operational, tactical and strategic decisions can be made based on the findings of the evolutionary method in use. Therefore, there is an urgent need for understanding the nature of these problems and the role of the stochastic fitness to gain insight into the possible performance of different methods. This paper presents a first attempt at characterizing the search space difficulties in red teaming to shed light on the expected performance of the evolutionary method in stochastic environments.

A Comprehensive Review of Shepherding as a Bio-Inspired Swarm-Robotics Guidance Approach

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 08-2020

DOI: 10.1109/TETCI.2020.2992778

Toward Electroencephalographic Profiling of Player Motivation: A Survey

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 09-2018

DOI: 10.1109/TCDS.2017.2726083

A scenario-based evolutionary scheduling approach for assessing future supply chain fleet capabilities

Publisher: Springer Berlin Heidelberg

Date: 2007

DOI: 10.1007/978-3-540-48584-1_18

Evolving Stories: Tree Adjoining Grammar Guided Genetic Programming for Complex Plot Generation

Publisher: Springer Berlin Heidelberg

Date: 2010

DOI: 10.1007/978-3-642-17298-4_14

The Limits of Reactive Shepherding Approaches for Swarm Guidance

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/ACCESS.2020.3037325

From Subjective to Objective Metrics for Evolutionary Story Narration Using Event Permutations

Publisher: Springer Berlin Heidelberg

Date: 2012

DOI: 10.1007/978-3-642-34859-4_40

Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges

Publisher: Springer Science and Business Media LLC

Date: 23-12-2015

DOI: 10.1007/S12559-015-9365-5

Connecting the dots to disconnect them: A study into network evolution and dynamics for analyzing terrorist networks

Publisher: SPIE

Date: 28-12-2006

DOI: 10.1117/12.644283

Mebra: multiobjective evolutionary-based risk assessment

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 08-2009

DOI: 10.1109/MCI.2009.933098

An expressive GL-2 grammar for representing story-like scenarios

Publisher: IEEE

Date: 06-2012

DOI: 10.1109/CEC.2012.6252888

Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 09-2018

DOI: 10.1109/TNSRE.2018.2864119

Motif Difficulty (MD): A Predictive Measure of Problem Difficulty for Evolutionary Algorithms Using Network Motifs

Publisher: MIT Press - Journals

Date: 09-2012

DOI: 10.1162/EVCO_A_00045

Abstract: One of the major challenges in the field of evolutionary algorithms (EAs) is to characterise which kinds of problems are easy and which are not. Researchers have been attracted to predict the behaviour of EAs in different domains. We introduce fitness landscape networks (FLNs) that are formed using operators satisfying specific conditions and define a new predictive measure that we call motif difficulty (MD) for comparison-based EAs. Because it is impractical to exhaustively search the whole network, we propose a s ling technique for calculating an approximate MD measure. Extensive experiments on binary search spaces are conducted to show both the advantages and limitations of MD. Multidimensional knapsack problems (MKPs) are also used to validate the performance of approximate MD on FLNs with different topologies. The effect of two representations, namely binary and permutation, on the difficulty of MKPs is analysed.

Application of chaos measures to a simplified boids flocking model

Publisher: Springer Science and Business Media LLC

Date: 29-01-2015

DOI: 10.1007/S11721-015-0103-0

Online generation of trajectories for autonomous vehicles using a multi-agent system

Publisher: IEEE

Date: 07-2014

DOI: 10.1109/CEC.2014.6900345

Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks

Publisher: IGI Global

Date: 2006

DOI: 10.4018/978-1-59140-670-9.CH002

Abstract: Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the latest research areas in this field is evolving ANNs. In this chapter, we investigate the simultaneous evolution of network architectures and connection weights in ANNs. In simultaneous evolution, we use the well-known concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional ANN algorithms. It is expected that this methodology would provide better solutions to many applications of ANNs.

Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 12-2019

DOI: 10.1109/TIFS.2019.2916403

A Deep Hierarchical Reinforcement Learner for Aerial Shepherding of Ground Swarms

Publisher: Springer International Publishing

Date: 2019

DOI: 10.1007/978-3-030-36708-4_54

Swarm Metaverse for Multi-Level Autonomy Using Digital Twins

Publisher: MDPI AG

Date: 19-05-2023

DOI: 10.3390/S23104892

Abstract: Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human–swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.

Mixed strategy and coevolution dynamics in social networks

Publisher: Elsevier BV

Date: 2011

DOI: 10.1016/J.PHYSA.2010.09.031

Society of Mind cognitive agent architecture applied to drivers adapting in a traffic context

Publisher: SAGE Publications

Date: 11-12-2013

DOI: 10.1177/1059712313509652

Abstract: This paper challenges the idea of creating an architectural approach for designing a complex hybrid cognitive agent, i.e., an agent that possesses all reactive, deliberative and affective capabilities, outside the hierarchical paradigm. Thus, it presents an agent architecture based on Marvin Minsky’s ‘Society of Mind’ (SoM) metaphor on human mind and cognition, and shows how SoM agents, through the features implicitly generated by their design, allow a tremendous power of representation of human heterogeneity and variety of behaviours. In order to show that, the SoM general architecture is instantiated and evaluated in a cognitively demanding environment: as a driver agent in a traffic behaviour context.

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-10-2020

DOI: 10.36227/TECHRXIV.12834002

Abstract: Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep. br

The discrete gradient evolutionary strategy method for global optimization

Publisher: IEEE

Date: 2003

DOI: 10.1109/CEC.2003.1299608

Evolutionary dynamics of interdependent exogenous risks

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/CEC.2010.5586429

On the channel density of EEG signals for reliable biometric recognition

Publisher: Elsevier BV

Date: 07-2021

DOI: 10.1016/J.PATREC.2021.04.003

A neuroscientific approach to choice modeling: electroencephalogram (EEG) and user preferences

Publisher: IEEE

Date: 06-2012

DOI: 10.1109/IJCNN.2012.6252361

Competency awareness in strategic decision making

Publisher: IEEE

Date: 02-2011

DOI: 10.1109/COGSIMA.2011.5753426

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2019

DOI: 10.1109/ACCESS.2019.2902812

Risk assessment of capability requirements using WISDOM-II

Publisher: SPIE

Date: 28-12-2005

DOI: 10.1117/12.644273

Mixture of Spectral Generative Adversarial Networks for Imbalanced Hyperspectral Image Classification

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2022

DOI: 10.1109/LGRS.2020.3041864

Local-Global Interaction and the Emergence of Scale-Free Networks with Community Structures

Publisher: MIT Press

Date: 10-2011

DOI: 10.1162/ARTL_A_00038

Abstract: Understanding complex networks in the real world is a nontrivial task. In the study of community structures we normally encounter several ex les of these networks, which makes any statistical inferencing a challenging endeavor. Researchers resort to computer-generated networks that resemble networks encountered in the real world as a means to generate many networks with different sizes, while maintaining the real-world characteristics of interest. The generation of networks that resemble the real world turns out in itself to be a complex search problem. We present a new rewiring algorithm for the generation of networks with unique characteristics that combine the scale-free effects and community structures encountered in the real world. The algorithm is inspired by social interactions in the real world, whereby people tend to connect locally while occasionally they connect globally. This local-global coupling turns out to be a powerful characteristics that is required for our proposed rewiring algorithm to generate networks with community structures, power law distributions both in degree and in community size, positive assortative mixing by degree, and the rich-club phenomenon.

Fleet estimation for defence logistics using a multi-objective learning classifier system

Publisher: ACM

Date: 12-07-2011

DOI: 10.1145/2001576.2001738

Evolving Story Narrative Using Surrogate Models of Human Judgement

Publisher: Springer Berlin Heidelberg

Date: 2013

DOI: 10.1007/978-3-642-37374-9_62

Weighted Gate Layer Autoencoders

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 08-2022

DOI: 10.1109/TCYB.2021.3049583

Bio-inspired robotics for air traffic weather information management

Publisher: SAGE Publications

Date: 12-07-2010

DOI: 10.1177/0142331210366688

Abstract: With the increase in automation to serve the growing needs and challenges of aviation, air traffic controllers (ATCs) are now faced with an information overload from a myriad of sources, both in graphical and textual format. One such source is weather information, which is typically comprised of wind speed, wind direction, thunderstorms, cloud cover, icing, temperature and pressure at various altitudes. This information requires domain expertise to interpret and communicate to ATCs, who then employ this information to manage air traffic efficiently and safely. Unfortunately, ATCs are not trained meteorologists, so there are significant challenges associated with the correct interpretation and utilization of this information by ATCs. In this paper, we propose a bio-inspired weather robot, which interacts with the air traffic environment and provides targeted weather-related information to ATCs by identifying which airspace sectors they are working on. It uses bio-inspired techniques for processing weather information and path planning in the air traffic environment and is fully autonomous in the sense that it only interacts with the air traffic environment passively and has an onboard weather information processing system. The weather robot system was evaluated in an experimental environment with live Australian air traffic, where it successfully navigated the environment, processed weather information, identified airspace sectors and delivered weather-related information for the relevant sector using a synthetic voice.

A machine education approach to swarm decision-making in best-of-n problems

Publisher: Springer Science and Business Media LLC

Date: 22-11-2021

DOI: 10.1007/S11721-021-00206-5

Dual Phase Evolution

Publisher: Springer New York

Date: 2014

DOI: 10.1007/978-1-4419-8423-4

Stopping criteria for ensemble of evolutionary artificial neural networks

Publisher: Elsevier BV

Date: 11-2005

DOI: 10.1016/J.ASOC.2004.12.005

Hierarchical Deep Reinforcement Learning for Continuous Action Control

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 11-2018

DOI: 10.1109/TNNLS.2018.2805379

All hazards analysis: A complexity perspective

Publisher: IGI Global

Date: 2005

DOI: 10.4018/978-1-59140-640-2.CH001

Abstract: With the increase in the complexity of terrorism’s networks and activities, the advances in chemical and biological warfare, and the use of organized criminal activities, it is becoming apparent that dealing with this complexity is not possible with traditional problem-solving approaches. The artificial complexity area (Artificial Life, or ALife), complex systems and agent-based distillation (ABD) provide a new perspective to the problem and emphasize the importance of modeling the interaction between system components to tackle these issues. This chapter presents an introduction to Cellular Automota and ABD, and then reviews and critiques how these approaches specifically have been used to model aspects of bushfires, epidemics, biological warfare and terrorism. This chapter then extends upon previous works to present an overview of the possible use of artificial complexity models to the larger field of security and safety applications.

Spatio-temporal dynamics of security investments in an interdependent risk environment

Publisher: Elsevier BV

Date: 10-2012

DOI: 10.1016/J.PHYSA.2012.05.020

How Hard Is It To Red Team?

Publisher: IGI Global

Date: 2006

DOI: 10.4018/978-1-59140-640-2.CH003

Abstract: Red teaming is the process of studying a problem by anticipating adversary behaviors. When done in simulations, the behavior space is ided into two groups: one controlled by the red team, which represents the set of adversary behaviors or bad guys the other controlled by the blue team, which represents the set of defenders or good guys. Through red teaming, analysts can learn about the future by forward prediction of scenarios. More recently, defense has been looking at evolutionary computation methods in red teaming. The fitness function in these systems is highly stochastic, where a single configuration can result in multiple outcomes. Operational, tactical and strategic decisions can be made based on the findings of the evolutionary method in use. Therefore, there is an urgent need to understand the nature of these problems and the role of the stochastic fitness to gain insight into the possible performance of different methods. This chapter presents a first attempt at characterizing the search space difficulties in red teaming to shed light on the expected performance of the evolutionary method in stochastic environments.

A model of symbiomemesis: machine education and communication as pillars for human-autonomy symbiosis

Publisher: The Royal Society

Date: 16-08-2021

DOI: 10.1098/RSTA.2020.0364

Abstract: Symbiosis is a physiological phenomenon where organisms of different species develop social interdependencies through partnerships. Artificial agents need mechanisms to build their capacity to develop symbiotic relationships. In this paper, we discuss two pillars for these mechanisms: machine education (ME) and bi-directional communication. ME is a new revolution in artificial intelligence (AI) which aims at structuring the learning journey of AI-enabled autonomous systems. In addition to the design of a systematic curriculum, ME embeds the body of knowledge necessary for the social integration of AI, such as ethics, moral values and trust, into the evolutionary design and learning of the AI. ME promises to equip AI with skills to be ready to develop logic-based symbiosis with humans and in a manner that leads to a trustworthy and effective steady-state through the mental interaction between humans and autonomy a state we name symbiomemesis to differentiate it from ecological symbiosis. The second pillar, bi-directional communication as a discourse enables information to flow between the AI systems and humans. We combine machine education and communication theory as the two prerequisites for symbiosis of AI agents and present a formal computational model of symbiomemesis to enable symbiotic human-autonomy teaming. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

Multiobjectivity and Complexity in Embodied Cognition

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 08-2005

DOI: 10.1109/TEVC.2005.846902

Computational Red Teaming: Past, Present and Future

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2011

DOI: 10.1109/MCI.2010.939578

Walking with EMO

Publisher: IGI Global

Date: 2008

DOI: 10.4018/978-1-59904-498-9.CH011

Abstract: This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO) techniques. The main objective of this chapter is to demonstrate practical ways of generating simple legged locomotion for simulated robots with two, four and six legs using EMO. The operational performance as well as complexities of the resulting evolved Pareto solutions that act as controllers for these robots will then be analyzed. Additionally, the operational dynamics of these evolved Pareto controllers in noisy and uncertain environments, limb dynamics and effects of using a different underlying EMO algorithm will also be discussed.

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2016

DOI: 10.1109/ACCESS.2016.2571058

Improving the Performance of Genetic Algorithm in Capacitated Vehicle Routing Problem using Self Imposed Constraints

Publisher: IEEE

Date: 04-2007

DOI: 10.1109/SCIS.2007.367693

A Methodology for Synthesizing Interdependent Multichannel EEG Data with a Comparison Among Three Blind Source Separation Techniques

Publisher: Springer International Publishing

Date: 2015

DOI: 10.1007/978-3-319-26561-2_19

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-10-2020

DOI: 10.36227/TECHRXIV.12834002.V2

Abstract: Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep. br

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 27-08-2020

DOI: 10.36227/TECHRXIV.12834002.V1

Abstract: Boids (Bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (Sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog to model predation risk in predator-prey dynamic. Previous work assumed homogeneous shoids. Real-world observations on sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoid with different parameters from the remainder of the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from actual sheep herding trial by an Unmanned Aerial Vehicle (UAV) to validate the proposed measures. The resultant footprints will allow us in our future research to design more efficient control strategies for the UAV to improve the herding of sheep, by polarising the attention of the machine learning algorithm on those Shoids with influence footprints to drive the flock. / / /

Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 03-2021

DOI: 10.1109/TCYB.2019.2939399

A novel mixture of experts model based on cooperative coevolution

Publisher: Elsevier BV

Date: 12-2006

DOI: 10.1016/J.NEUCOM.2006.04.009

Discovery Projects - Grant ID: DP200101211

Start Date: 2020

End Date: 12-2024

Amount: $580,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP110100488

Start Date: 03-2011

End Date: 03-2014

Amount: $315,000.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0561255

Start Date: 2005

End Date: 12-2006

Amount: $14,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP140102590

Start Date: 2014

End Date: 12-2017

Amount: $399,178.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0987832

Start Date: 01-2009

End Date: 06-2012

Amount: $360,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0667123

Start Date: 07-2006

End Date: 12-2009

Amount: $306,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP160102037

Start Date: 2016

End Date: 12-2021

Amount: $560,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0453657

Start Date: 2004

End Date: 06-2009

Amount: $514,034.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354623

Start Date: 01-2004

End Date: 11-2004

Amount: $20,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0989506

Start Date: 10-2009

End Date: 12-2009

Amount: $500,000.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0459557

Start Date: 12-2004

End Date: 12-2010

Amount: $1,950,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100143

Start Date: 08-2011

End Date: 08-2012

Amount: $500,000.00

Funder: Australian Research Council

ARC Centres Of Excellence - Grant ID: CE0348249

Start Date: 2003

End Date: 06-2009

Amount: $5,208,295.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0460006

Start Date: 07-2004

End Date: 06-2009

Amount: $1,500,000.00

Funder: Australian Research Council