Decision support systems (DSS) are usually based on computerised models of biophysical and economic systems. Despite early expectations that such models would inform and improve management, adoption rates have been low, and implementation of DSS is now “critical” The reasons for this are unclear and the aim of this study is to learn to better design, develop and apply DSS in farming systems research (FSR). Previous studies have explored the merits of quantitative tools including DSS, and suggested changes leading to greater impact. In Australia, the changes advocated have been: Simple, flexible, low cost economic tools: Emphasis on farmer learning through soft systems approaches: Understanding the socio-cultural contexts of using and developing DSS: Farmer and researcher co-learning from simulation modelling and Increasing user participation in DSS design and implementation. Twenty-four simple criteria were distilled from these studies, and their usefulness in guiding the development and application of DSS were assessed in six FSR case studies. The case studies were also used to better understand farmer learning through models of decision making and learning. To make DSS useful complements to farmers’ existing decision-making repertoires, they should be based on: (i) a decision-oriented development process, (ii) identifying a motivated and committed audience, (iii) a thorough understanding of the decision-makers context, (iv) using learning as the yardstick of success, and (v) understanding the contrasts, contradictions and conflicts between researcher and farmer decision cultures / Doctor of Philosophy (PhD)

Australian farmers are supplementing traditional practices with innovative strategies in an effort to survive recent economic, environmental, and social crises in the rural sector. These innovative strategies include moving towards a technology-based farm management style. A review of past literature determines that, despite a growing awareness of the usefulness of computers for farm management, there is concern over the limited demand for computer-based agricultural decision support systems (DSS). Recent literature indicates that women are the dominant users of computers on family farms yet are hesitant to use computers for decision support, and it is also unclear what decision-making roles women assume on family farms. While past research has investigated the roles of women in the Australian rural sector, there is a dearth of research into the interaction of women cotton growers with computers. Therefore, this dissertation is an ontological study and aims to contribute to scholarly knowledge in the research domain of Australian women cotton growers, agricultural DSS, and cotton farm management. This dissertation belongs in the Information Systems (IS) stream and describes an interpretive single case study which explores the lives of Australian women cotton growers on family farms and the association of an agricultural DSS with their farm management roles. Data collection was predominantly through semi-structured interviews with women cotton growers and cotton industry professionals such as DSS developers, rural extension officers, researchers and educators, rural experimental scientists, and agronomists and consultants, all of whom advise cotton growers. The study was informed by multiple sociological theories with opposing paradigmatic assumptions: Giddens' (1984) structuration theory as a metatheory to explore the recursiveness of farm life and technology usage; Rogers' (1995) diffusion of innovations theory with a functionalist approach to objectively examine the features of the software and user, as well as the processes of technology adoption; and Connell's (2002) theory of gender relations with its radical humanist perspective to subjectively investigate the relationships between farm partners through critical enquiry. The study was enriched further by drawing on other writings of these authors (Connell 1987; Giddens 2001; Rogers 2003) as well as complementary theories by authors (Orlikowski 1992; Orlikowski 2000; Trauth 2002; Vanclay & Lawrence 1995). These theories in combination have not been used before, which is a theoretical contribution of the study. The agricultural DSS for the study was CottonLOGIC, an advanced farm management tool to aid the management of cotton production. It was developed in the late 1990s by the CSIRO and the Australian Cotton Cooperative Research Centre (CRC), with support from the Cotton Research and Development Corporation (CRDC). CottonLOGIC is a software package of decision support and record-keeping modules to assist cotton growers and their advisors in the management of cotton pests, soil nutrition, and farm operations. It enables the recording and reporting of crop inputs and yields, insect populations (heliothis, tipworm, mirids and so on), weather data, and field operations such as fertiliser and pesticide applications, as well as the running of insect density prediction (heliothis and mites) and soil nutrition models. The study found that innovative practices and sustainable solutions are an imperative in cotton farm management for generating an improved triple bottom line of economic, environmental and social outcomes. CottonLOGIC is an industry benchmark for supporting these values through the incorporation of Best Management Practices (BMP) and Integrated Pest Management (IPM) principles, although there were indications that the software is in need of restructuring as could be expected of software over five years old. The evidence from the study was that women growers are participants in strategic farm decisions but less so in operational decisions, partly due to their lack of relevant agronomic knowledge. This hindered their use of CottonLOGIC, despite creative attempts to modify it. The study endorsed the existence of gender differences and inequalities in rural Australia. Nevertheless, the study also found that the women are valued for their roles as business partners in the multidisciplinary nature of farm management. All the same, there was evidence that greater collaboration and cooperation by farm partners and advisors would improve business outcomes. On the whole, however, women cotton growers are not passive agents but take responsibility for their own futures. In particular, DSS tools such as CottonLOGIC are instrumental in enabling women cotton growers to adapt to, challenge, and influence farm management practices in the family farm enterprise, just as CottonLOGIC is itself shaped and reshaped. Hence, a practical contribution of this study is to provide non-prescriptive guidelines for the improved adoption of agricultural DSS, particularly by rural women, as well as increasing awareness of the worth of their roles as family farm business partners.

Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents' track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified.

This study is an examination of residential mobility and its outcomes focussing on the forced relocation of public housing tenants from The Parks area of metropolitan Adelaide. In Euro-American countries, this type of residential mobility is increasingly used as a means of facilitating urban regeneration and countering the effects of the ongoing decrease in local public housing stock. The result is growing numbers of public tenants affected by relocation. The study agues that these public tenants have the right to a basic level of residential satisfaction, and in order for this satisfaction to be provided; the conditions and character of its formation must be understood. The thesis examines residential mobility and the formation of residential satisfaction to provide a basis for understanding the outcomes and effects of relocation, who is most affected, and how to target solutions to improve the relocation process. Despite the fact that households experience similar influences, and make their residential decisions in largely predictable ways, the formulation of residential satisfaction and the effects of relocation are highly individualised. Successful relocation is shown to be dependent on the inclusion of tenants' expert knowledge about their own residential satisfaction; this means that resident involvement in the process is crucial. This thesis investigates a means of combining these findings to improve the outcome of the relocation process for each individual tenant and their household. A prototype Spatial Decision Support System (SDSS) is constructed to allow relocating tenants to participate in their own relocation decision process. This SDSS allows local, spatially referenced information to be combined with each tenants own expert knowledge. This information is combined through a structured decision process, which is presented in a portable computer program with a simplified user interface. This SDSS is tested by relocating tenants and key stakeholders from The Parks to evaluate its usefulness in improving the relocation process. / Thesis (Ph.D.)--Geography and Environmental Studies, 2002.

Decision support systems (DSS) are usually based on computerised models of biophysical and economic systems. Despite early expectations that such models would inform and improve management, adoption rates have been low, and implementation of DSS is now “critical” The reasons for this are unclear and the aim of this study is to learn to better design, develop and apply DSS in farming systems research (FSR). Previous studies have explored the merits of quantitative tools including DSS, and suggested changes leading to greater impact. In Australia, the changes advocated have been: Simple, flexible, low cost economic tools: Emphasis on farmer learning through soft systems approaches: Understanding the socio-cultural contexts of using and developing DSS: Farmer and researcher co-learning from simulation modelling and Increasing user participation in DSS design and implementation. Twenty-four simple criteria were distilled from these studies, and their usefulness in guiding the development and application of DSS were assessed in six FSR case studies. The case studies were also used to better understand farmer learning through models of decision making and learning. To make DSS useful complements to farmers’ existing decision-making repertoires, they should be based on: (i) a decision-oriented development process, (ii) identifying a motivated and committed audience, (iii) a thorough understanding of the decision-makers context, (iv) using learning as the yardstick of success, and (v) understanding the contrasts, contradictions and conflicts between researcher and farmer decision cultures / Doctor of Philosophy (PhD)

<p>Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. Parameters useful to the system are suggested. Some of these, such as age and gender, should be provided by the physician, and some should be derived from electro- and phonocardiographic signals.</p><p>Various methods of signal processing, such as wavelet theory and principal components analysis, are described. Heart failure should be diagnosed based on the parameters, and so various forms of decision support systems, such as neural networks and support vector machines, are described. The methods of signal processing and classification are discussed and suggestions on how to develop the system are made.</p>

<p>This thesis presents the development of a prototype of the Baseline Oriented Modeling</p><p>(BOM) approach, called protoBOM.</p><p>BOM is a framework that semi-automatically generates Decision Support Systems in a</p><p>specific domain, based on Software Product Lines.</p><p>protoBOM semi-automatically generates applications as PRISMA architectural models by using Model-Driven Architecture and Software Product Line techniques. These models are automatically compiled and the object code (C#, in .NET) is generated obtaining an executable application.</p><p>In protoBOM, the user constructs Decision Support Systems in a simpler way by using the</p><p>ontologies of the diagnosis and the application domains by means of Domain Specific Languages. The interfaces will be closer to the problem domain, which will facilitate user interaction in a manner simple and intuitive.</p>

Modern civil commercial transport aircraft provide the means for the safest of all forms of transportation. While advanced computer technology ranging from flight management computers to warning and alerting devices contributed to flight safety significantly, it is undisputed that the flightcrew represents the most frequent primary cause factor in airline accidents. From a system perspective, machine actors such as the autopilot and human actors (the flightcrew) try to achieve goals (desired states of the aircraft). The set of activities to achieve a goal is called a function. In modern flightdecks both machine actors and human actors perform functions. Recent accident studies suggest that deficiencies in the flightcrew's ability to monitor how well either machines or themselves perform a function are a factor in many accidents and incidents. As humans are inherently bad monitors, this study proposes a method to automatically assess the status of a function in order to increase flight safety as part of an intelligent pilot aid, called the AgendaManager. The method was implemented for the capture altitude function: seeking to attain and maintain a target altitude. Fuzzy systems were used to compute outputs indicating how well the capture altitude function was performed from inputs describing the state of the aircraft. In order to conform to human expert assessments, the fuzzy systems were trained using a genetic algorithm (GA) whose objective was to minimize the discrepancy between system outputs and human expert assessments based on 72 scenarios. The resulting systems were validated by analyzing how well they conformed to new data drawn from another 32 scenarios. The results of the study indicated that even though the training procedure facilitated by the GA was able to improve conformance to human expert assessments, overall the systems performed too poorly to be deployed in a real environment. Nevertheless, experience and insights gained from the study will be valuable in the development of future automated systems to perform function assessment. / Graduation date: 1998

Modern commercial air travel is considered by most transportation specialists to be the safest of all forms of transportation. While safe, any loss of life is tragic and the only really acceptable state of commercial air transport safety is that of 'zero-tolerance' where no accident is acceptable. Research has demonstrated that the largest single causal component for airline accidents is the flightcrew. In addition, the recent automation of these machines has created many new safety concerns involving flightcrew situational awareness, human-machine interfacing, workload, attention, and complexity, to name a few. These concerns led to a series of studies developing, refining, and testing numerous aspects of this issue. The studies incorporated ASRS (Aviation Safety Reporting System) incident reports, NTSB (National Transportation Safety Board) and other accident reports, and the development of a CTM (cockpit task management) system. The information gained from this research led to the development of an agent-based cockpit task aiding system termed the AgendaManager. A traditional text-based display similar to that used in the CTMS study was developed, optimized, and integrated with existing systems like EICAS (Engine Indicating and Crew Alerting System) using a visual display development guide developed from a literature review. An alternate display incorporating graphics and located on the primary flight display (PFD) was also developed in an effort to improve pilot agenda management performance. Both of the interfaces were developed using a comprehensive visual display design guide compiled through a literature review. The Agenda Manager displays were tested in order to determine if the PFD enhancements improved agenda management performance. Eleven general aviation pilots participated in the study, three in the pilot study and eight in the main study. Results from the main study indicate little, if any, difference in agenda management performance in regards to the display format used. In general, the study demonstrated the usefulness of the display guidelines, importance of tracking instrument rating when using general aviation pilots in an experiment, and the equivalence of 'round-trip' scenarios. / Graduation date: 1998