A Nature inspired guidance system for unmanned autonomous vehicles employed in a search role.

Since the very earliest days of the human race, people have been studying animal behaviours. In those early times, being able to predict animal behaviour gave hunters the advantages required for success. Then, as societies began to develop this gave way, to an extent, to agriculture and early studies, much of it trial and error, enabled farmers to successfully breed and raise livestock to feed an ever growing population. Following the advent of scientific endeavour, more rigorous academic research has taken human understanding of the natural world to much greater depth. In recent years, some of this understanding has been applied to the field of computing, creating the more specialised field of natural computing. In this arena, a considerable amount of research has been undertaken to exploit the analogy between, say, searching a given problem space for an optimal solution and the natural process of foraging for food. Such analogies have led to useful solutions in areas such as numerical optimisation and communication network management, prominent examples being ant colony systems and particle swarm optimisation; however, these solutions often rely on well-defined fitness landscapes that may not always be available. One practical application of natural computing may be to create behaviours for the control of autonomous vehicles that would utilise the findings of ethological research, identifying the natural world behaviours that have evolved over millennia to surmount many of the problems that autonomous vehicles find difficult; for example, long range underwater navigation or obstacle avoidance in fast moving environments. This thesis provides an exploratory investigation into the use of natural search strategies for improving the performance of autonomous vehicles operating in a search role. It begins with a survey of related work, including recent developments in autonomous vehicles and a ground breaking study of behaviours observed within the natural world that highlights general cooperative group behaviours, search strategies and communication methods that might be useful within a wider computing context beyond optimisation, where the information may be sparse but new paradigms could be developed that capitalise on research into biological systems that have developed over millennia within the natural world. Following this, using a 2-dimensional model, novel research is reported that explores whether autonomous vehicle search can be enhanced by applying natural search behaviours for a variety of search targets. Having identified useful search behaviours for detecting targets, it then considers scenarios where detection is lost and whether natural strategies for re-detection can improve overall systemic performance in search applications. Analysis of empirical results indicate that search strategies exploiting behaviours found in nature can improve performance over random search and commonly applied systematic searches, such as grids and spirals, across a variety of relative target speeds, from static targets to twice the speed of the searching vehicles, and against various target movement types such as deterministic movement, random walks and other nature inspired movement. It was found that strategies were most successful under similar target-vehicle relationships as were identified in nature. Experiments with target occlusion also reveal that natural reacquisition strategies could improve the probability oftarget redetection

A nature inspired guidance system for unmanned autonomous vehicles employed in a search role

Since the very earliest days of the human race, people have been studying animal behaviours. In those early times, being able to predict animal behaviour gave hunters the advantages required for success. Then, as societies began to develop this gave way, to an extent, to agriculture and early studies, much of it trial and error, enabled farmers to successfully breed and raise livestock to feed an ever growing population. Following the advent of scientific endeavour, more rigorous academic research has taken human understanding of the natural world to much greater depth. In recent years, some of this understanding has been applied to the field of computing, creating the more specialised field of natural computing. In this arena, a considerable amount of research has been undertaken to exploit the analogy between, say, searching a given problem space for an optimal solution and the natural process of foraging for food. Such analogies have led to useful solutions in areas such as numerical optimisation and communication network management, prominent examples being ant colony systems and particle swarm optimisation; however, these solutions often rely on well-defined fitness landscapes that may not always be available. One practical application of natural computing may be to create behaviours for the control of autonomous vehicles that would utilise the findings of ethological research, identifying the natural world behaviours that have evolved over millennia to surmount many of the problems that autonomous vehicles find difficult; for example, long range underwater navigation or obstacle avoidance in fast moving environments. This thesis provides an exploratory investigation into the use of natural search strategies for improving the performance of autonomous vehicles operating in a search role. It begins with a survey of related work, including recent developments in autonomous vehicles and a ground breaking study of behaviours observed within the natural world that highlights general cooperative group behaviours, search strategies and communication methods that might be useful within a wider computing context beyond optimisation, where the information may be sparse but new paradigms could be developed that capitalise on research into biological systems that have developed over millennia within the natural world. Following this, using a 2-dimensional model, novel research is reported that explores whether autonomous vehicle search can be enhanced by applying natural search behaviours for a variety of search targets. Having identified useful search behaviours for detecting targets, it then considers scenarios where detection is lost and whether natural strategies for re-detection can improve overall systemic performance in search applications. Analysis of empirical results indicate that search strategies exploiting behaviours found in nature can improve performance over random search and commonly applied systematic searches, such as grids and spirals, across a variety of relative target speeds, from static targets to twice the speed of the searching vehicles, and against various target movement types such as deterministic movement, random walks and other nature inspired movement. It was found that strategies were most successful under similar target-vehicle relationships as were identified in nature. Experiments with target occlusion also reveal that natural reacquisition strategies could improve the probability oftarget redetection.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Self-Adaptive Software with Decentralised Control Loops

We present DECIDE, a rigorous approach to decentralising the control loops of distributed self-adaptive software used in mission-critical applications. DECIDE uses quantitative verification at runtime, first to agree individual component contributions to meeting system-level quality-of-service requirements, and then to ensure that components achieve their agreed contributions in the presence of changes and failures. All verification operations are carried out locally, using component-level models, and communication between components is infrequent. We illustrate the application of DECIDE and show its effectiveness using a case study from the unmanned underwater vehicle domain

Stochastic modeling, analysis and verification of mission-critical systems and processes

Software and business processes used in mission-critical defence applications are often characterised by stochastic behaviour. The causes for this behaviour range from unanticipated environmental changes and built-in random delays to component and communication protocol unreliability. This paper overviews the use of a stochastic modelling and analysis technique called quantitative verication to establish whether mission-critical software and business processes meet their reliability, performance and other quality-of-service requirements

Utilising Assured Multi-Agent Reinforcement Learning within safety-critical scenarios

Multi-agent reinforcement learning allows a team of agents to learn how to work together to solve complex decision-making problems in a shared environment. However, this learning process utilises stochastic mechanisms, meaning that its use in safety-critical domains can be problematic. To overcome this issue, we propose an Assured Multi-Agent Reinforcement Learning (AMARL) approach that uses a model checking technique called quantitative verification to provide formal guarantees of agent compliance with safety, performance, and other non-functional requirements during and after the reinforcement learning process. We demonstrate the applicability of our AMARL approach in three different patrolling navigation domains in which multi-agent systems must learn to visit key areas by using different types of reinforcement learning algorithms (temporal difference learning, game theory, and direct policy search). Furthermore, we compare the effectiveness of these algorithms when used in combination with and without our approach. Our extensive experiments with both homogeneous and heterogeneous multi-agent systems of different sizes show that the use of AMARL leads to safety requirements being consistently satisfied and to better overall results than standard reinforcement learning

The Operator Product Expansion of N=4 SYM and the 4-point Functions of Supergravity

We give a detailed Operator Product Expansion interpretation of the results for conformal 4-point functions computed from supergravity through the AdS/CFT duality. We show that for an arbitrary scalar exchange in AdS(d+1) all the power-singular terms in the direct channel limit (and only these terms) exactly match the corresponding contributions to the OPE of the operator dual to the exchanged bulk field and of its conformal descendents. The leading logarithmic singularities in the 4-point functions of protected N=4 super-Yang Mills operators (computed from IIB supergravity on AdS(5) X S(5) are interpreted as O(1/N^2) renormalization effects of the double-trace products appearing in the OPE. Applied to the 4-point functions of the operators Ophi ~ tr F^2 + ... and Oc ~ tr FF~ + ..., this analysis leads to the prediction that the double-trace composites [Ophi Oc] and [Ophi Ophi - Oc Oc] have anomalous dimension -16/N^2 in the large N, large g_{YM}^2 N limit. We describe a geometric picture of the OPE in the dual gravitational theory, for both the power-singular terms and the leading logarithms. We comment on several possible extensions of our results.Comment: 42 page

Reliability Assessment and Safety Arguments for Machine Learning Components in Assuring Learning-Enabled Autonomous Systems

The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LES) -- has resulted in the pressing need to assure their functional safety. As for traditional functional safety, the emerging consensus within both, industry and academia, is to use assurance cases for this purpose. Typically assurance cases support claims of reliability in support of safety, and can be viewed as a structured way of organising arguments and evidence generated from safety analysis and reliability modelling activities. While such assurance activities are traditionally guided by consensus-based standards developed from vast engineering experience, LES pose new challenges in safety-critical application due to the characteristics and design of ML models. In this article, we first present an overall assurance framework for LES with an emphasis on quantitative aspects, e.g., breaking down system-level safety targets to component-level requirements and supporting claims stated in reliability metrics. We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers that utilises the operational profile and robustness verification evidence. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM and propose practical solutions. Probabilistic safety arguments at the lower ML component-level are also developed based on the RAM. Finally, to evaluate and demonstrate our methods, we not only conduct experiments on synthetic/benchmark datasets but also demonstrate the scope of our methods with a comprehensive case study on Autonomous Underwater Vehicles in simulation