This thesis investigates the conceptual and methodological issues for the development of Intelligent Spatial Decision Support Systems (ISDSS). These are spatial decision support systems (SDSS) integrating intelligent systems techniques (Genetic Algorithms, Neural Networks, Expert Systems, Fuzzy Logic and Nonlinear methods) with traditional modelling and statistical methods for the analysis of spatial problems. The principal aim of this work is to verify the feasibility of heterogeneous systems for spatial decision support derived from a combination of traditional numerical techniques and intelligent techniques in order to provide superior performance and functionality to that achieved through the use of traditional methods alone. This thesis is composed of four distinct sections: (i) a taxonomy covering the employment of intelligent systems techniques in specific applications of geographical information systems and SDSS; (ii) the development of a prototype ISDSS; (iii) application of the prototype ISDSS to modelling the spatiotemporal dynamics of high technology industry in the South-East of England; and (iv) the development of ISDSS architectures utilising interapplication communication techniques. Existing approaches for implementing modelling tools within SDSS and GIS generally fall into one of two schemes - loose coupling or tight coupling - both of which involve a tradeoff between generality and speed of data interchange. In addition, these schemes offer little use of distributed processing resources. A prototype ISDSS was developed in collaboration with KPMG Peat Marwick's High Technology Practice as a general purpose spatiotemporal analysis tool with particular regard to modelling high technology industry. The GeoAnalyser system furnishes the user with animation and time plotting tools for observing spatiotemporal dynamics; such tools are typically not found in existing SDSS or GIS. Furthermore, GeoAnalyser employs the client/server model of distributed computing to link the front end client application with the back end modelling component contained within the server application. GeoAnalyser demonstrates a hybrid approach to spatial problem solving - the application utilises a nonlinear model for the temporal evolution of spatial variables and a genetic algorithm for calibrating the model in order to establish a good fit for the dataset under investigation. Several novel architectures are proposed for ISDSS based on existing distributed systems technologies. These architectures are assessed in terms of user interface, data and functional integration. Implementation issues are also discussed. The research contributions of this work are four-fold: (i) it lays the foundation for ISDSS as a distinct type of system for spatial decision support by examining the user interface, performance and methodological requirements of such systems; (ii) it explores a new approach for linking modelling techniques and SDSS; (iii) it investigates the possibility of modelling high technology industry; and (iv) it details novel architectures for ISDSS based on distributed systems.

Type-1 fuzzy logic has, for over thirty years, provided an approach for modelling uncertainty and imprecision. This methodology has been highly successful with a history of successful applications in a number of areas - particularly control. However, type-1 fuzzy systems are essentially `crisp' in nature. This is not only paradoxical but also raises concerns for knowledge representation and inferencing. In particular type-1 fuzzy logic is flawed when representing perceptions such as colour, beauty, comfort etc. since these perceptions do not have a measurable domain. This fundamental paradox is tackled in this research by employing a type-2 fuzzy paradigm. The investigation of the type-2 approach concludes that the uncertainty or imprecision that exists in most real problems can be more effectively modelled by a type-2 approach. The research reported in this thesis explores the properties of type-2 fuzzy sets as well as showing how useful they can be for knowledge representation and inferencing. It is shown that type-2 fuzzy sets have an important role to play in modelling perceptions. Results are given of using type-2 fuzzy sets to represent perceptions of a medical expert for shin image analysis indicating that the type-2 fuzzy paradigm is particularly helpful for perception representation. A methodology has been developed that allows linguistic inputs to an adaptive system that implements a type-2 fuzzy system(the Adaptive Fuzzy Perception Learner (AFPL)). In this thesis, the rationale and full mathematical detail of the AFPL is presented. The approach has been applied successfully to the, so called, linguistic AND (analogous to the Boolean AND) as an aid to illustrating the methodology. Results are presented of applying the method to a real problem of classifying the acceptability of a car based on perceptions that describe certain features of the car. The AFPL is applied to this large, complex, set of data where the inputs to the network are linguistic. A detailed evaluation of the AFPL is given with recommendations for effective use of the AFPL. The results indicate that we now, truly, have an approach for learning the perceptions and relations in a type-2 fuzzy system

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This thesis is a conceptual and empirical approach to embody modelling of continuous sequential behaviour in neural learning. The aim is to enhance the feasibility of training and capacity for generalisation. By examining the sequential aspects of the passing of time in a neural network, it is suggested that an alteration to the usual goal weight condition may be made to model these aspects. The notion of a goal weight path is introduced, with a path-based backpropagation (PBP) framework being proposed. Two models using PBP have been investigated in the thesis. One is called Feedforward Continuous BackPropagation (FCBP) which is a generalization of conventional BackPropagation; the other is called Recurrent Continuous BackPropagation (RCBP) which provides a neural dynamic system for I/O associations. Both models make use of the continuity underlying analogue-binary associations and analogue-analogue associations within a fixed neural network topology. A graphical simulator cbptool for Sun workstations has been designed and implemented for supporting the research. The capabilities of FCBP and RCBP have been explored through experiments. The results for FCBP and RCBP confirm the modelling theory. The fundamental alteration made on conventional backpropagation brings substantial improvement in training and generalization to enhance the power of backpropagation.

This thesis explores the behaviour-oriented nature of intelligence and presents the definition and use of Fuzzy Behavioural Maps (FBMs) as a flexible development framework for providing complex autonomous agent behaviour. This thesis provides a proof-of-concept for simple FBMs, including some experimental results in Mobile Robotics and Fuzzy Logic Control. This practical work shows the design of a collision avoidance behaviour (of a mobile robot) using a simple FBM and, the implementation of this using a Fuzzy Logic Controller (FLC). The FBM incorporates three causally related sensorimotor activities (moving around, perceiving obstacles and, varying speed). This Collision Avoidance FBM is designed (in more detail) using fuzzy relations (between levels of perception, motion and variation of speed) in the form of fuzzy control rules. The FLC stores and manipulates these fuzzy control (FBM) rules using fuzzy inference mechanisms and other related implementation parameters (fuzzy sets and fuzzy logic operators). The resulting FBM-FLC architecture controls the behaviour patterns of the agent. Its fuzzy inference mechanisms determine the level of activation of each FBM node while driving appropriate control actions over the creature's motors. The thesis validates (demonstrates the general fitness of) this control architecture through various pilot tests (computer simulations). This practical work also serves to emphasise some benefits in the use of FLC techniques to implement FBMs (e.g. flexibility of the fuzzy aggregation methods and fuzzy granularity).More generally, the thesis presents and validates a FBM Framework to develop more complex autonomous agent behaviour. This framework represents a top-down approach to derive the BB models using generic FBMs, levels of abstraction and refinement stages. Its major scope is to capture and model behavioural dynamics at different levels of abstraction (through different levels of refinement). Most obviously, the framework maps some required behaviours into connection structures of behaviour-producing modules that are causally related. But the main idea is following as many refinement stages as required to complete the development process. These refinement stages help to identify lower design parameters (i.e. control actions) rather than linguistic variables, fuzzy sets or, fuzzy inference mechanisms. They facilitate the definition of the behaviours selected from first levels of abstraction. Further, the thesis proposes taking the FBM Framework into the implementation levels that are required to build BB control architecture and provides and application case study. This describes how to develop a complex, non-hierarchical, multi-agent behaviour system using the refinement capabilities of the FBM Framework. Finally, the thesis introduces some more general ideas about the use of this framework to cope with some, current complexity issues around the behaviour-oriented nature of intelligence.

<p> This thesis argues that cross-lingual word sense disambiguation (CL-WSD) can be used to improve lexical selection for machine translation when translating from a resource-rich language into an under-resourced one, especially when relatively little bitext is available. In CL-WSD, we perform word sense disambiguation, considering the senses of a word to be its possible translations into some target language, rather than using a sense inventory developed manually by lexicographers. </p><p> Using explicitly trained classifiers that make use of source-language context and of resources for the source language can help machine translation systems make better decisions when selecting target-language words. This is especially the case when the alternative is hand-written lexical selection rules developed by researchers with linguistic knowledge of the source and target languages, but also true when lexical selection would be performed by a statistical machine translation system, when there is a relatively small amount of available target-language text for training language models. </p><p> In this work, I present the Chipa system for CL-WSD and apply it to the task of translating from Spanish to Guarani and Quechua, two indigenous languages of South America. I demonstrate several extensions to the basic Chipa system, including techniques that allow us to benefit from the wealth of available unannotated Spanish text and existing text analysis tools for Spanish, as well as approaches for learning from bitext resources that pair Spanish with languages unrelated to our intended target languages. Finally, I provide proof-of-concept integrations of Chipa with existing machine translation systems, of two completely different architectures.</p><p>

In modem high-rise buildings, a suitable control algorithm has to be chosen so that lifts can respond to passenger requests in such a way as to transport them quickly and efficiently to their destinations. The aim of the current work is to assess new scheduling approaches and intelligent monitoring techniques in order to aid the design of new lift systems and to improve the performance of existing installations. To achieve this, the project has been divided into three major parts. Firstly, a model of passenger movements has been developed from an analysis of data gathered from installed lift systems, thereby allowing the realistic simulation of landing calls, car calls and door opening times. Secondly, a lift simulator has been produced to allow the modular comparison of alternative scheduling and monitoring approaches and to provide an accurate model of lift dynamics. Thirdly, a new intelligent lift scheduling system has been implemented.

This thesis presents a study of a novel approach for supporting students in text based electronic conferencing. It describes the development of a concept known as the Virtual Participant. An initial prototype was developed which was tested on the Open University Business School MBA course on Creative Management. The Virtual Participant first presented itself to the users as Uncle Bulgaria. a metaphor for collecting and recycling important information. The Virtual Participant approach is to store the discussions students have had in previous years that the course has run. and to retrieve those discussions at a time most appropriate to helping the students studying this year. It was never intended to provide 'the answer' but rather examples of similar discussions on similar topics. Uncle Bulgaria interacted with the students over a period of 16 weeks. during which time the students prepared two assignments and completed the first half of the course. The information gained from the students' interactions with the system and their feedback to a questionnaire survey was then fed back into a second prototype' which was again tested on the same course. In the second study the system was known to the students as the Active Archive. an active component of an archive of past student discussions. Through cross year comparisons it was possible to evaluate the improvements made between the Active Archive and Uncle Bulgaria systems. The Active Archive interacted with the students on a much larger scale than Uncle Bulgaria had. but with no increased negative impact. The second study provided examples where the Active Archive stimulated discussion amongst the students and vicarious learning could be said to have taken place. Taking the lessons learned from these two studies a number of guidelines for the development of such systems have been produced and are described and discussed.

<p> Even as electronic payments and virtual currencies become more popular, checks are still the nearly ubiquitous form of payment for many situations in the United States such as payroll, purchasing a vehicle, paying rent, and hiring a contractor. Fraud has always plagued this form of payment, and this research aimed to capture the scope of this 15^th century problem in the 21^st century. Today, counterfeit checks originating from overseas are the scourge of online dating sites, classifieds forums, and mailboxes throughout the country. Additional frauds including alteration, theft, and check kiting also exploit checks. Check fraud is causing hundreds of millions in estimated losses to both financial institutions and consumers annually, and the problem is growing. Fraud investigators and financial institutions must be better educated and armed to successfully combat it. This research study collected information on the history of checks, forms of check fraud, victimization, and methods for check fraud prevention and detection. Check fraud is not only a financial issue, but also a social one. Uneducated and otherwise vulnerable consumers are particularly targeted by scammers exploiting this form of fraud. Racial minorities, elderly, mentally ill, and those living in poverty are disproportionately affected by fraud victimization. Financial institutions struggle to strike a balance between educating customers, complying with regulations, and tailoring alerts that are both valuable and fast. Applications of artificial intelligence including machine learning and computer vision have many recent advancements, but financial institution anti-fraud measures have not kept pace. This research concludes that the onus rests on financial institutions to take a modern approach to check fraud, incorporating machine learning into real-time reviews, to adequately protect victims.</p><p>

abstract: Achieving human level intelligence is a long-term goal for many Artificial Intelligence (AI) researchers. Recent developments in combining deep learning and reinforcement learning helped us to move a step forward in achieving this goal. Reinforcement learning using a delayed reward mechanism is an approach to machine intelligence which studies decision making with control and how a decision making agent can learn to act optimally in an environment-unaware conditions. Q-learning is one of the model-free reinforcement directed learning strategies which uses temporal differences to estimate the performances of state-action pairs called Q values. A simple implementation of Q-learning algorithm can be done using a Q table memory to store and update the Q values. However, with an increase in state space data due to a complex environment, and with an increase in possible number of actions an agent can perform, Q table reaches its space limit and would be difficult to scale well. Q-learning with neural networks eliminates the use of Q table by approximating the Q function using neural networks. Autonomous agents need to develop cognitive properties and become self-adaptive to be deployable in any environment. Reinforcement learning with Q-learning have been very efficient in solving such problems. However, embedded systems like space rovers and autonomous robots rarely implement such techniques due to the constraints faced like processing power, chip area, convergence rate and cost of the chip. These problems present a need for a portable, low power, area efficient hardware accelerator to accelerate the process of such learning. This problem is targeted by implementing a hardware schematic architecture for Q-learning using Artificial Neural networks. This architecture exploits the massive parallelism provided by neural network with a dedicated fine grain parallelism provided by a Field Programmable Gate Array (FPGA) thereby processing the Q values at a high throughput. Mars exploration rovers currently use Xilinx-Space-grade FPGA devices for image processing, pyrotechnic operation control and obstacle avoidance. The hardware resource consumption for the architecture has been synthesized considering Xilinx Virtex7 FPGA as the target device. / Dissertation/Thesis / Masters Thesis Engineering 2016