Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution

Shepherding involves herding a swarm of agents (\emph{sheep}) by another a control agent (\emph{sheepdog}) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using \emph{way points} on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved

Evolutionary Rule Learning for High Dimensional Classification Problems: Investigations and Remedies

This thesis investigates the problem of high-dimensional data classification using evolutionary rule learning algorithms. High-dimensional data analysis problems are now commonplace due to the rapid advancement in technology which has resulted in the collection of large data sets in various domains. A systematic analysis of LCS, widely accepted as the flagship evolutionary rule learning algorithm, is performed to determine the causes leading to its performance degradation in high-dimensional classification problems. First, empirical investigation of the performance of a supervised LCS in increasing dimension real-valued classification problems is performed. The systematic study allows us to establish that the extant learning bounds are necessary but are not sufficient to meet the challenge of higher dimensional real-valued spaces. A theoretical analysis is then conducted that derives new learning bounds for system convergence in these problems.A novel divide-and-conquer methodology is developed that aims at improving system scalability using the mathematically grounded rough set theory. The proposed model, RELCS, provides a solution to the sparsity problem caused by high dimensionality through projecting high-dimensional feature space into a set of smaller but representational sub-spaces. Then, a set of LCSs are trained independently on each of these sub-spaces. Finally, the outputs from each LCS in the ensemble are fused to generate the final prediction.A novel decoupling approach is proposed that aims at addressing the stalling of genetic search in high-dimensional spaces, caused by data sparsity and forgetting. A Michigan-style evolutionary rule learning system is developed that aims at modifying the multivariate search problem faced by the genetic algorithm in LCSs to a univariate search problem. This is achieved by decoupling the two search processes (finding optimal feature bounds, conjuncts, and their optimal combination, disjunct, for each rule) conducted implicitly by a genetic algorithm in LCSs. This approach allows keeping the nice features of LCS, that is the online learning and interpretability, intact while significantly reducing the load on system resources. The resultant system provides significantly better generalization capabilities as well as model complexity. The results of different parts of the research are tested and validated using a proposed synthetic binary classification problem and real-world problems

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks

Generating Collective Motion Behaviour Libraries using Developmental Evolution

This paper presents an evolutionary framework for generating diverse libraries of collective motion behaviours. It builds upon recent advancements in machine recognition of collective motion and the transformation of random motions into structured collective behaviours. The paper describes the design of the framework, including the use of a fitness function and diversity metrics specifically tailored for this purpose. The proposed framework generates diverse behaviours with distinct collective motion characteristics. Analysing the relationship between genotypic and behavioural diversity, we observed that greater diversity emerges after a moderate number of evolutionary generations. Our findings highlight the effectiveness of task non-specific fitness functions in distinguishing structured collective behaviours in an evolutionary setting

Human-Swarm-Teaming Transparency and Trust Architecture

Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature. This is due, in part, to the lack of an agreed definition and the overlap between the connected - sometimes used synonymously - concepts of interpretability and explain ability. We assert that transparency is the overarching concept, with the tenets of interpretability, explainability, and predictability subordinate. We draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture (HST3-Architecture). The architecture reinforces transparency as a key contributor towards situation awareness, and consequently as an enabler for effective trustworthy human-swarm teaming (HST).</p