Expert systems for fault analysis

The initial aim of this research was to investigate the application of expert Systems, or Knowledge Base Systems technology to the automated synthesis of Hazard and Operability Studies. Due to the generic nature of Fault Analysis problems and the way in which Knowledge Base Systems work, this goal has evolved into a consideration of automated support for Fault Analysis in general, covering HAZOP, Fault Tree Analysis, FMEA and Fault Diagnosis in the Process Industries. This thesis described a proposed architecture for such an Expert System. The purpose of the System is to produce a descriptive model of faults and fault propagation from a description of the physical structure of the plant. From these descriptive models, the desired Fault Analysis may be produced. The way in which this is done reflects the complexity of the problem which, in principle, encompasses the whole of the discipline of Process Engineering. An attempt is made to incorporate the perceived method that an expert uses to solve the problem; keywords, heuristics and guidelines from techniques such as HAZOP and Fault Tree Synthesis are used. In a truly Expert System, the performance of the system is strongly dependent on the high quality of the knowledge that is incorporated. This expert knowledge takes the form of heuristics or rules of thumb which are used in problem solving. This research has shown that, for the application of fault analysis heuristics, it is necessary to have a representation of the details of fault propagation within a process. This helps to ensure the robustness of the system - a gradual rather than abrupt degradation at the boundaries of the domain knowledge.

Testing incremental politics : the case of North Sea oil

In the quest to secure the much vaunted benefits of North Sea oil, highly non-incremental technologies have been adopted. Nowhere is this more the case than with the early fields of the central and northern North Sea. By focusing on the inflexible nature of North Sea hardware, in such fields, this thesis examines the problems that this sort of technology might pose for policy making. More particularly, the following issues are raised. First, the implications of non-incremental technical change for the successful conduct of oil policy is raised. Here, the focus is on the micro-economic performance of the first generation of North Sea oil fields and the manner in which this relates to government policy. Secondly, the question is posed as to whether there were more flexible, perhaps more incremental policy alternatives open to the decision makers. Conclusions drawn relate to the degree to which non-incremental shifts in policy permit decision makers to achieve their objectives at relatively low cost. To discover cases where non-incremental policy making has led to success in this way, would be to falsify the thesis that decision makers are best served by employing incremental politics as an approach to complex problem solving.

A cybernetic methodology to study and design human activities

This thesis offers a methodology to study and design effective communication mechanisms in human activities. The methodology is focused in the management of complexity. It is argued that complexity is not something objective that can be worked out analytically, but something subjective that depends on the viewpoint. Also it is argued that while certain social contexts may inhibit, others may enhance the viewpoint's capabilities to deal with complexity. Certain organisation structures are more likely than others to allow individuals to release their potentials. Thus, the relevance of studying and designing effective organisations. The first part of the thesis offers a `cybernetic methodology' for problem solving in human activities, the second offers a `method' to study and design organisations. The cybernetics methodology discussed in this work is rooted in second order cybernetics, or the cybernetics of the observing systems (Von Foester 1979, Maturana and Varela 1980). Its main tenet is that the known properties of the real world reside in the individual and not in the world itself. This view, which puts emphasis in a, by nature, one sided and unilateral appreciation of reality, triggers the need for dialogue and conversations to construct it. The `method' to study and design organisations, it based on Beer's Viable System Model (Beer 1979, 1981, 1985). This model permits us to assess how successful is an organisation in coping with its environmental complexity, and, moreover, permits us to establish how to make more effective the responses to this complexity. These features of the model are of great significance in a world where complexity is perceived to be growing at an unthinkable pace. But, `seeing' these features of the model assumes an effective appreciation of organisational complexity; hence the need for the methodological discussions offered by the first part of the thesis.

Disciplinary talk:a systemic functional exploration of university seminar discussions

Despite the growth of spoken academic corpora in recent years, relatively little is known about the language of seminar discussions in higher education. This thesis compares seminar discussions across three disciplinary areas. The aim of this thesis is to uncover the functions and patterns of talk used in different disciplinary discussions and to highlight language on a macro and micro level that would be useful for materials design and teaching purposes. A framework for identifying and analysing genres in spoken language based on Hallidayan Systemic Functional Linguistics (SFL) is used. Stretches of talk sharing a similar purpose and predictable functional staging, termed Discussion Macro Genres (DMGs) are identified. Language is compared across DMGs and across disciplines through use of corpus techniques in conjunction with SFL genre theory. Data for the study comprises just over 180,000 tokens and is drawn from the British Academic Spoken English corpus (BASE), recorded at two universities in the UK. The discipline areas investigated are Arts and Humanities, Social Sciences and Physical Sciences. Findings from this study make theoretical, empirical and methodological contributions to the field of spoken EAP. The empirical findings are firstly, that the majority of the seminar discussion can be assigned to one of the three main DMG in the corpus: Responding, Debating and Problem Solving. Secondly, it characterises each discipline area according to two DMGs. Thirdly, the majority of the discussion is non-oppositional in nature, suggesting that ‘debate’ is not the only form of discussion that students need to be prepared for. Finally, while some characteristics of the discussion are tied to the DMG and common across disciplines, others are discipline specific. On a theoretical level, this study shows that an SFL genre model for investigating spoken discourse can be successfully extended to investigate longer stretches of discourse than have previously been identified. The methodological contribution is to demonstrate how corpus techniques can be combined with SFL genre theory to investigate extended stretches of spoken discussion. The thesis will be of value to those working in the field of teaching spoken EAP/ ESAP as well as to materials developers.

A method for systematic artifact selection decision making

Artifact selection decisions typically involve the selection of one from a number of possible/candidate options (decision alternatives). In order to support such decisions, it is important to identify and recognize relevant key issues of problem solving and decision making (Albers, 1996; Harris, 1998a, 1998b; Jacobs & Holten, 1995; Loch & Conger, 1996; Rumble, 1991; Sauter, 1999; Simon, 1986). Sauter classifies four problem solving/decision making styles: (1) left-brain style, (2) right-brain style, (3) accommodating, and (4) integrated (Sauter, 1999). The left-brain style employs analytical and quantitative techniques and relies on rational and logical reasoning. In an effort to achieve predictability and minimize uncertainty, problems are explicitly defined, solution methods are determined, orderly information searches are conducted, and analysis is increasingly refined. Left-brain style decision making works best when it is possible to predict/control, measure, and quantify all relevant variables, and when information is complete. In direct contrast, right-brain style decision making is based on intuitive techniques—it places more emphasis on feelings than facts. Accommodating decision makers use their non-dominant style when they realize that it will work best in a given situation. Lastly, integrated style decision makers are able to combine the left- and right-brain styles—they use analytical processes to filter information and intuition to contend with uncertainty and complexity.

Filling knowledge gaps:knowledge sharing across inter-firm boundaries and occupational communities

This paper explores the micro-level processes of interaction across organisational boundaries and occupational communities. Based on a retrospective processual analysis, this study shows that in filling knowledge gaps, organisations put in place a series of knowledge mechanisms, which lead them to socially interact with their alliance partners. Both the deployment of existing knowledge and the creation of new knowledge are based on processes of interaction, which derive from the interplay between alliance actors. It is suggested that through both social interaction and the use of boundary objects, individuals are able to communicate, engage in problem-solving activities and share their ideas to fill knowledge gaps.

Exploring supply chain related graduate programmes in the UK:a view on their structure, content and approaches

Research in skill requirements needed by supply chain/logistics (SCL) managers has been published since the early nineties, however, research on what is really taught (e.g. curriculum, learning philosophies) by universities is scant. This paper's aim is to fill in this gap by analysing SCL graduate teaching in the UK. Data from 50 SCL MSc programmes were collected from 43 universities. Findings indicate that there seems to be a gap emerging between industry's needs and the content of the programmes being offered. This gap concerns employability, problem based learning, international business and the acquisition of softer interpersonal and problem solving skills.

New measures of job control, cognitive demand and production responsibility

Recent research has highlighted several job characteristics salient to employee well-being and behavior for which there are no adequate generally applicable measures. These include timing and method control, monitoring and problem-solving demand, and production responsibility. In this article, an attempt to develop measures of these constructs provided encouraging results. Confirmatory factor analyses applied to data from 2 samples of shop-floor employees showed a consistent fit to a common 5-factor measurement model. Scales corresponding to each of the dimensions showed satisfactory internal and test–retest reliabilities. As expected, the scales also discriminated between employees in different jobs and employees working with contrasting technologies.

Diversity in goal-orientation, team reflexivity, and team performance

Although recent research highlights the role of team member goalorientation in team functioning, research has neglected the effects of diversity in goalorientation. In a laboratory study with groups working on a problem-solving task, we show that diversity in learning and performanceorientation are related to decreased group performance. Moreover, we find that the effect of diversity in learning orientation is mediated by group information elaboration and the effect of diversity in performanceorientation by group efficiency. In addition, we demonstrate that teamreflexivity can counteract the negative effects of diversity in goalorientation. These results suggest that models of goal orientation in groups should incorporate the effects of diversity in goal orientation.

KnoFuss:a comprehensive architecture for knowledge fusion

We propose a knowledge fusion architecture KnoFuss based on the application of problem-solving methods technology, which allows methods for subtasks of the fusion process to be combined and the best methods to be selected, depending on the domain and task at hand.

A method for systematic artifact selection decision making

Artifact selection decisions typically involve the selection of one from a number of possible/candidate options (decision alternatives). In order to support such decisions, it is important to identify and recognize relevant key issues of problem solving and decision making (Albers, 1996; Harris, 1998a, 1998b; Jacobs & Holten, 1995; Loch & Conger, 1996; Rumble, 1991; Sauter, 1999; Simon, 1986). Sauter classifies four problem solving/decision making styles: (1) left-brain style, (2) right-brain style, (3) accommodating, and (4) integrated (Sauter, 1999). The left-brain style employs analytical and quantitative techniques and relies on rational and logical reasoning. In an effort to achieve predictability and minimize uncertainty, problems are explicitly defined, solution methods are determined, orderly information searches are conducted, and analysis is increasingly refined. Left-brain style decision making works best when it is possible to predict/control, measure, and quantify all relevant variables, and when information is complete. In direct contrast, right-brain style decision making is based on intuitive techniques—it places more emphasis on feelings than facts. Accommodating decision makers use their non-dominant style when they realize that it will work best in a given situation. Lastly, integrated style decision makers are able to combine the left- and right-brain styles—they use analytical processes to filter information and intuition to contend with uncertainty and complexity.

Engineering the future:CDIO as a tool for combating retention difficulties

With the demand for engineering graduates at what may be defined as an unprecedented high, many universities find themselves facing significant levels of student attrition-with high "drop-out levels" being a major issue in engineering education. In order to address this, Aston University in the UK has radically changed its undergraduate engineering education curriculum, introducing capstone CDIO (Conceive, Design, Implement, Operate) modules for all first year students studying Mechanical Engineering and Design. The introduction of CDIO is aimed at making project / problem based learning the norm. Utilising this approach, the learning and teaching in engineering purposefully aims to promote innovative thinking, thus equipping students with high-level problem-solving skills in a way that builds on theory whilst enhancing practical competencies and abilities. This chapter provides an overview of an Action Research study undertaken contemporaneously with the development, introduction, and administration of the first two semesters of CDIO. It identifies the challenges and benefits of the approach and concludes by arguing that whilst CDIO is hard work for staff, it can make a real difference to students' learning experiences, thereby positively impacting retention. © 2012, IGI Global.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

A taxonomy of heterogeneity and dynamics in particle swarm optimisation

We propose a taxonomy for heterogeneity and dynamics of swarms in PSO, which separates the consideration of homogeneity and heterogeneity from the presence of adaptive and non-adaptive dynamics, both at the particle and swarm level. It thus supports research into the separate and combined contributions of each of these characteristics. An analysis of the literature shows that most recent work has focussed on only parts of the taxonomy. Our results agree with prior work that both heterogeneity and dynamics are useful. However while heterogeneity does typically improve PSO, this is often dominated by the improvement due to dynamics. Adaptive strategies used to generate heterogeneity may end up sacrificing the dynamics which provide the greatest performance increase. We evaluate exemplar strategies for each area of the taxonomy and conclude with recommendations.

Evolutionary market agents for resource allocation in decentralised systems

We introduce self-interested evolutionary market agents, which act on behalf of service providers in a large decentralised system, to adaptively price their resources over time. Our agents competitively co-evolve in the live market, driving it towards the Bertrand equilibrium, the non-cooperative Nash equilibrium, at which all sellers charge their reserve price and share the market equally. We demonstrate that this outcome results in even load-balancing between the service providers. Our contribution in this paper is twofold; the use of on-line competitive co-evolution of self-interested service providers to drive a decentralised market towards equilibrium, and a demonstration that load-balancing behaviour emerges under the assumptions we describe. Unlike previous studies on this topic, all our agents are entirely self-interested; no cooperation is assumed. This makes our problem a non-trivial and more realistic one.