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This thesis presents a study into conflicts that emerge amongst sensor device rules when such devices are formed into networks. It describes conflicting patterns of communication and computation that can disturb the monitoring of subjects, and lower the quality of service. Such conflicts can negatively affect the lifetimes of the devices and cause incorrect information to be reported. A novel approach to detecting and resolving conflicts is presented. The approach is considered within the context of home-based psychiatric Ambulatory Assessment (AA). Rules are considered that can be used to control the behaviours of devices in a sensor network for AA. The research provides examples of rule conflict that can be found for AA sensor networks. Sensor networks and AA are active areas of research and many questions remain open regarding collaboration amongst collections of heterogeneous devices to collect data, process information in-network, and report personalised findings. This thesis presents an investigation into reliable rule-based service provisioning for a variety of stakeholders, including care providers, patients and technicians. It contributes a collection of rules for controlling AA sensor networks. This research makes a number of contributions to the field of rule-based sensor networks, including areas of knowledge representation, heterogeneous device support, system personalisation, and in particular, system reliability. This thesis provides evidence to support the conclusion that conflicts can be detected and resolved in adaptable rule-based sensor networks.

Knowledge representation and inference are the backbone of artificial intelligence, and logic programming is one of the most widely used knowledge representation tools. Logic programming with deduction/induction/abduction as the reasoning technique is serving numerous fields of artificial intelligence. In dynamic domains where there are constant changes in knowledge, updating the knowledge base is crucial to keep it stable. This thesis investigates the issues in updating the knowledge base. Two types of logic program based updates are considered, simple fact based updates where the knowledge base is updated by a simple fact, and rule based updates where the knowledge base is updated by a rule. A SLDNF based procedural approach is proposed to implement such updates. This thesis also investigates the issues involved in simple fact based and rule based abduction, and it is observed that updates are closely related to abduction. A SLDNF based procedural approach to perform simple fact/rule based updates and abduction is proposed as a result of this study / Master of Science (Hons)

In recent years, many researches focused on the supervised machine learning classification methods using Lidar and remotely sensed image to provide buildings, trees, roads, and grass categories for urban ground feature classification. First, this research performed urban ground feature classification based on true color aerial imagey and Lidar Intensity. Second, Lidar derived normalized DSM (nDSM) was added to the classification. Finally, the concept of height level rules was applied. This research utilized two-level height rule-based classification exteneded from three-level height rule-based classification (Huang, 2007). It is obvious to observ the overlap for the roads and houses, and grass and trees in the feature space plot where result in the classification confusion. These confusions can be resolved by fusion the height information. After comparing classification accuracy, the two-level height is better than three-level height classification scheme. This research proposed hybrid classification method based on Maximum likelihood classification (MLC) and two-level height rules. This method reveals the role of height information in urban ground feature classification. The height level rules were also applied to other supervised classification method such as Back-Propagation Network (BPN) and Support Vector Machine (SVM). The classification results show that the accuracy of hybrid method is better than the orgional classification method. However, the time required to look for the classification parameters for BPN and SVM is greater than MLC but only can derived considerable results. Therefore, the hybrid classification method based on MLC is better than other two methods.

We explore the direct and inverse problem of thermodynamics in the context of rule-based modelling. The direct problem can be concisely stated as obtaining a set of rewriting rules and their rates from the description of the energy landscape such that their asymptotic behaviour when t → ∞ coincide. To tackle this problem, we describe an energy function as a finite set of connected patterns P and an energy cost function e which associates real values to each of these energy patterns. We use a finite set of reversible graph rewriting rules G to define the qualitative dynamics by showing which transformations are possible. Given G and P, we construct a finite set of rules Gp which i) has the same qualitative transition system as G and ii) when equipped with rates according to e, defines a continuous-time Markov chain that has detailed balance with respect to the invariant probability distribution determined by the energy function. The construction relies on a technique for rule refinement described in earlier work and allows us to represent thermodynamically consistent models of biochemical interaction networks in a concise manner. The inverse problem, on the other hand, is to i) check whether a rule-based model has an energy function that describes its asymptotic behaviour and if so ii) obtain the energy function from the graph rewriting rules and their rates. Although this problem is known to be undecidable in the general case, we find two suitable subsets of Kappa, our rule-based modelling framework of choice, were this question can be answer positively and the form of their energy functions described analytically.

This exploratory study investigates the hypothesis that case-based reasoning (CBR) systems have advantages over rule-based reasoning (RBR) systems in providing automated support for Tier-2 help desk operations. The literature suggests that rule-based systems are best suited for problem solving when the system being analyzed is a single-purpose, specialized system and the rules for solving the problems are clear and do not change with high frequency. Case-based systems, because of their ability to offer alternative solutions for a given problem, give help-desk technicians more flexibility. Specifically, this dissertation aims to answer the following questions: 1. Which paradigm, rule-based or case-based reasoning, results in more precise solutions to problems when compared to the solutions derived from system manuals? 2. Which paradigm, rule-based or case-based reasoning, is more convenient to maintain in terms of knowledge modification (i.e. addition, deletion, or modification of rules/cases)? 3. Which paradigm, rule-based or case-based reasoning, enables help-desk technicians to solve problems in shorter time, and therefore at lower cost? This is an exploratory study based on data collected from field experiments. RBR and CBR based prototypes were set up to support Tier-2 help desk operations. Trained help desk operators used the system to solve a set of benchmark problems. Data collected from this exercise was analyzed to answer the three research questions. This exploratory study supported the hypothesis that the case-based paradigm is better suited for use in help desk environments at the Tier-2 level than is the rule-based paradigm. The case-based paradigm, because of its ability to offer alternative solutions for a given problem, gave the help-desk technician flexibility in applying a solution. Alternatively, the rule-based paradigm provided a solution if, and only if, a rule existed for a solution meeting the exact problem specifications. Further, in the absence of a rule, problem research time, using the rule-based paradigm, extended the time required to formulate a solution thereby increasing the cost. This research provided sufficient information to show that the help-desk knowledge based system utilizing the case-based shell provided better overall solutions to problems than did the rule-based shell.

Computational biology seeks to understand complex spatio-temporal phenomena across multiple levels of structural and functional organisation. However, questions raised in this context are difficult to answer without modelling methodologies that are intuitive and approachable for non-expert users. Stochastic rule-based modelling languages such as Kappa have been the focus of recent attention in developing complex biological models that are nevertheless concise, comprehensible, and easily extensible. We look at further developing Kappa, in terms of how we might define complex models in both the spatial and the temporal axes. In defining complex models in space, we address the assumption that the reaction mixture of a Kappa model is homogeneous and well-mixed. We propose evolutions of the current iteration of Spatial Kappa to streamline the process of defining spatial structures for different modelling purposes. We also verify the existing implementation against established results in diffusion and narrow escape, thus laying the foundations for querying a wider range of spatial systems with greater confidence in the accuracy of the results. In defining complex models over time, we draw attention to how non-modelling specialists might define, verify, and analyse rules throughout a rigorous model development process. We propose structured visual methodologies for developing and maintaining knowledge base data structures, incorporating the information needed to construct a Kappa rule-based model. We further extend these methodologies to deal with biological systems defined by the activity of synthetic genetic parts, with the hope of providing tractable operations that allow multiple users to contribute to their development over time according to their area of expertise. Throughout the thesis we pursue the aim of bridging the divide between information sources such as literature and bioinformatics databases and the abstracting decisions inherent in a model. We consider methodologies for automating the construction of spatial models, providing traceable links from source to model element, and updating a model via an iterative and collaborative development process. By providing frameworks for modellers from multiple domains of expertise to work with the language, we reduce the entry barrier and open the field to further questions and new research.

Bottomland hardwood forests are highly productive ecosystems which perform many important ecological services. Unfortunately, many bottomland hardwood forests have been degraded or lost. Accurate land cover mapping is crucial for management decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was combined with Light Detection and Ranging (LiDAR) data from 2006 and several ancillary datasets to map a portion of the bottomland hardwood system found in the Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased classification techniques, and object-based classification techniques were used to distinguish nine land cover types in the area. The rule-based classification (84.41% overall accuracy) outperformed the other classification methods because it more effectively incorporated the LiDAR and ancillary datasets when needed. This output was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine abundance trends in the area’s bottomland hardwood forests. The classifications from 1974-1991 were conducted using identical class definitions and input imagery (Landsat MSS 60m), and the direct comparison demonstrates an overall declining trend in bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was used. However, when the classifications are re-sampled to the same resolution (60m), the percent area of bottomland hardwood consistently decreases from 1974-2005. Additional investigation of object-oriented classification proved useful. A major shortcoming of object-based classification is limited justification regarding the selection of segmentation parameters. Often, segmentation parameters are arbitrarily defined using general guidelines or are determined through a large number of parameter combinations. This research justifies the selection of segmentation parameters through a process that utilizes landscape metrics and statistical techniques to determine ideal segmentation parameters. The classification resulting from these parameters outperforms the classification resulting from arbitrary parameters by approximately three to six percent in terms of overall accuracy, demonstrating that landscape metrics can be successfully linked to segmentation parameters in order to create image objects that more closely resemble real-world objects and result in a more accurate final classification.

Thesis (M. Phil.)--University of Hong Kong, 1989.

published_or_final_version / Computer Science / Master / Master of Philosophy