Designing and implementing a new appraisal system for the Ministry of Defence Police.

When I started the project. I was Chief Inspector and the Head of Training for the Ministry of Defence (MoD), Police Training Centre (PTC) at Wethersfield in Essex. As the PTC was considered to be at the centre of the organisation, my role was seen to be that of a change agent. Therefore, I was an inside researcher. This raised two crucial methodological issues for the study. To ensure that the study was objective, reliable and valid, I had to take different approaches. Firstly, as the Head of Training, my role allowed me to quickly get to the core of the problem and I was able to generate extensive amount of crucial data. This challenged the organisational values and the power base of a hierarchical institute. The effect was complex, it meant that my learning was captured in a "personal way". This study produced Volume I, which explains the design, test, implementation of the new appraisal process and my learning. It also demonstrates my capabilities as an inside researcher who is able to shape major organisational change. Secondly, I had to use extensive collaborative approach to counteract the possibility of subjectivity which I encountered as an inside researcher. The collaborative approach produced Volume II, which is the study's tangible outcome. Volume II makes a professional impact on the whole of the Ministry of Defence Police (MDP) with a significant change in the organisations appraisal process. Further, as advocated by Bell (1997), being an inside researcher I had to maintain strict ethical standards at all times

The implementation of urban plans : the case of Medina Master Directive Plan, Saudi Arabia.

SIGLEAvailable from British Library Document Supply Centre-DSC:DX213288 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Accelerating decision making under partial observability using learned action priors

Thesis (M.Sc.)--University of the Witwatersrand, Faculty of Science, School of Computer Science and Applied Mathematics, 2017.Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework allowing a robot to reason about the consequences of actions and observations with respect to the agent's limited perception of its environment. They allow an agent to plan and act optimally in uncertain environments. Although they have been successfully applied to various robotic tasks, they are infamous for their high computational cost. This thesis demonstrates the use of knowledge transfer, learned from previous experiences, to accelerate the learning of POMDP tasks. We propose that in order for an agent to learn to solve these tasks quicker, it must be able to generalise from past behaviours and transfer knowledge, learned from solving multiple tasks, between di erent circumstances. We present a method for accelerating this learning process by learning the statistics of action choices over the lifetime of an agent, known as action priors. Action priors specify the usefulness of actions in situations and allow us to bias exploration, which in turn improves the performance of the learning process. Using navigation domains, we study the degree to which transferring knowledge between tasks in this way results in a considerable speed up in solution times. This thesis therefore makes the following contributions. We provide an algorithm for learning action priors from a set of approximately optimal value functions and two approaches with which a prior knowledge over actions can be used in a POMDP context. As such, we show that considerable gains in speed can be achieved in learning subsequent tasks using prior knowledge rather than learning from scratch. Learning with action priors can particularly be useful in reducing the cost of exploration in the early stages of the learning process as the priors can act as mechanism that allows the agent to select more useful actions given particular circumstances. Thus, we demonstrate how the initial losses associated with unguided exploration can be alleviated through the use of action priors which allow for safer exploration. Additionally, we illustrate that action priors can also improve the computation speeds of learning feasible policies in a shorter period of time.MT201

Reinforcement Learning Algorithms for Robotic Navigation in Dynamic Environments

Electrical Engineering Technolog

The humane containment of mentally disordered prisoners .

In many western countries, including New Zealand (NZ), the humane containment of mentally disordered prisoners (MOP) has became a topical issue in recent years. The current review of the literature demonstrated that there is little agreement regarding what measures are required to effectively address this somewhat complex matter and that there is a dearth of empirical research in this area, especially in NZ. The purpose of the current research was to empirically investigate this issue, within the framework of prisoners' rights in NZ, to ascertain whether there is an inherent gap between MOP's legal entitlements to mental health treatment (MHT) and the practical fulfilment of these rights. First, this was quantified by establishing an estimated base rate of clinically diagnosed MOP, and then by statistically verifying such persons’ access to MHT via an in-depth analysis of both the detection and referral process and the treatment delivery system currently in operation to service the mental health needs of prisoners at a local medium security men's prison in NZ. A randomised sample of one hundred medium security male inmates was obtained to ascertain a base rate of clinically MOP. Interviews were conducted, using the structured clinical interview for the OSM-111-R (SCIO). The results showed that 62.8% of the final sample (n=94) had a clinically diagnosed disorder and that co­ occurring disorders, especially substance use comorbidity, were prevailing issues. Analysis of the detection procedure involved a three-tiered examination; perusing the accuracy rate of detection, establishing what variables influenced a referral, and comparing these components with the referral process for rehabilitative MHT. The detection accuracy of participants with a substance use disorder was found to be significant, however, the detection rates of MOP and of participants requiring rehabilitative MHT for sexual or violent offence related issues were not found to function beyond the level of chance. Multivariate analysis revealed that visible signs of psychopathology, mental health history and atypical criminal characteristics increased the likelihood of detection. However, the majority of the MOP in this study were not detected. The examination of the treatment delivery system comprised a similar three-tiered analysis. The treatment delivery rate for MOP was not found to be statistical significant and neither was the overall delivery of rehabilitative MHT. Factors involved in treatment delivery were also explored and the findings collectively showed that the delivery of MHT for MOP primarily functioned as crisis intervention rather than humane containment. The practical implications of these research findings are discussed in relation to MOP's moral and legal entitlements to MHT and recommendations are made

Journey of Artificial Intelligence Frontier: A Comprehensive Overview

The field of Artificial Intelligence AI is a transformational force with limitless promise in the age of fast technological growth This paper sets out on a thorough tour through the frontiers of AI providing a detailed understanding of its complex environment Starting with a historical context followed by the development of AI seeing its beginnings and growth On this journey fundamental ideas are explored looking at things like Machine Learning Neural Networks and Natural Language Processing Taking center stage are ethical issues and societal repercussions emphasising the significance of responsible AI application This voyage comes to a close by looking ahead to AI s potential for human-AI collaboration ground-breaking discoveries and the difficult obstacles that lie ahead This provides with a well-informed view on AI s past present and the unexplored regions it promises to explore by thoroughly navigating this terrai

Space Station Mission Planning System (MPS) development study. Volume 2

The process and existing software used for Spacelab payload mission planning were studied. A complete baseline definition of the Spacelab payload mission planning process was established, along with a definition of existing software capabilities for potential extrapolation to the Space Station. This information was used as a basis for defining system requirements to support Space Station mission planning. The Space Station mission planning concept was reviewed for the purpose of identifying areas where artificial intelligence concepts might offer substantially improved capability. Three specific artificial intelligence concepts were to be investigated for applicability: natural language interfaces; expert systems; and automatic programming. The advantages and disadvantages of interfacing an artificial intelligence language with existing FORTRAN programs or of converting totally to a new programming language were identified

Privatisation and trade unions' mobilisation : a comparative study on the privatisation of the electricity industry in UK and Argentina : a test for mobilisation theory

EThOS - Electronic Theses Online ServiceGBUnited Kingdo