Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 107-112). Also available via the Internet.

Agents that act as user assistants will become invaluable as the number of information sources continue to proliferate. Such agents can support the work of users by learning to automate time-consuming tasks and filter information to manageable levels. Although considerable advances have been made in this area, it remains a fertile area for further development. One application of agents under careful scrutiny is the automated negotiation of conflicts between different user's needs and desires. Many techniques require explicit user models in order to function. This dissertation explores a technique for dynamically constructing user models and the impact of using them to anticipate the need for negotiation. Negotiation is reduced by including an advising aspect to the agent that can use this anticipation of conflict to adjust user behavior.

One of the greatest problems facing researchers in the sub field of Artificial Intelligence known as Intelligent Tutoring Systems (ITS) is the selection of a knowledge base designs that will facilitate the modification of the knowledge base. The Class-Entity-Relationship-Attribute (CERA), proposed by R. P. Brazile, holds certain promise as a more generic knowledge base design framework upon which can be built robust and efficient ITS. This study has a twofold purpose. The first is to demonstrate that a CERA knowledge base can be constructed for an ITS on a subset of the domain of Cretaceous paleontology and function as the "expert module" of the ITS. The second is to test the validity of the ideas that students guided through a lesson learn more factual knowledge, while those who explore the knowledge base that underlies the lesson through query at their own pace will be able to formulate their own integrative knowledge from the knowledge gained in their explorations and spend more time on the system. This study concludes that a CERA-based system can be constructed as an effective teaching tool. However, while an ITS - treatment provides for statistically significant gains in achievement test scores, the type of treatment seems not to matter as much as time spent on task. This would seem to indicate that a query-based system which allows the user to progress at their own pace would be a better type of system for the presentation of material due to the greater amount of on-line computer time exhibited by the users.

This dissertation consists of two articles. The first article describes an architecture for intelligent tutoring that focuses on modularity. This new architecture is based on Gibbons' layers theory for instructional design (2014). Splitting up the architecture for an intelligent tutor into layers allows different pieces to age at different rates which, in turn, allows the intelligent tutor to be adapted to new research and design theories. This architecture supports building intelligent tutoring services, nimble programs that can be assembled together to replicate the functions of intelligent tutoring without the expertise needed to create the services. Alternative architectures support building intelligent tutoring systems, monolithic programs that are less amenable to change and require immense expertise. The second article provides a proof of concept for the first services created under the layers theory. These two services create the building blocks of a domain and comprise one part of the content layer as described in the first article. The first service focuses on the task of key concept extraction whereas the second service focuses on prerequisite relationship extraction. These two tasks can provide the structure of the domain, particularly when it comes to domains that are more declarative in nature rather than procedural.

An apparel product (or “apparel”) is a human product. The design of an apparel product (or “apparel design”) should share many features of general product design and be conducted with a high degree of systematics and rationality. However, the current practice of apparel design is relatively more experience-based and ad-hoc than it should be. Besides, computer support to apparel design is quite limited in that there are several software systems available for supporting apparel design but they are isolated. Two reasons may explain this above situation: (1) absence of the ontology of apparel and apparel design, and (2) absence of a systematic and rational apparel design process. Furthermore, apparel is a specialized type of product in that all three inherent requirements (i.e., function, comfort related to ergonomics, and pleasure related to aesthetics) are equally important, especially the latter, which creates positive affects in the human wearer. In general, knowledge of how to design an apparel product for pleasure/affects is missing from the current design. The general motivation for the research conducted in this thesis is to locate and articulate this “missing knowledge” in order to advance design technology including computer-aided design for modern apparel products. The specific objectives of the research presented in this thesis are: (1) development of a model for the ontology of apparel or apparel system so that all basic concepts and their relationships related to the apparel system are captured; (2) development of a systematic design process for apparel that captures all the inherent characteristics of design, namely iteration and open-endedness; and (3) development of a computer-aided system for affective design for apparel, whereby human feeling once described can be computed with the result that an apparel product meets the wearer’s “feeling needs” (functional and ergonomic needs are assumed to be satisfied or not the concern of this thesis). There are several challenges to achieving the foregoing objectives. The first of these is the understanding of ontology for apparel and apparel design, given that there are so many types of apparel and ad-hoc apparel design processes in practice. The second challenge is the generalization and aggregation of the various ad-hoc apparel design processes that exist in practice. Third is the challenge presented by imprecise information and knowledge in the aspect of human’s affect. All three above challenges have been tackled and answered in this thesis. The first challenge is tackled with the tool of data modeling especially semantic-oriented data modeling. The second challenge is tackled with the general design theory such as general design phase theory, axiomatic design theory, and FCBPSS knowledge architecture (F: function, C: context, B: behavior, P: principle, SS: state and structure). The third challenge is tacked with the data mining technique and subjective rating technique. Several contributions are made with this thesis. First is the development of a comprehensive ontology model for apparel and apparel design that provides a basis for computer-aided design and manufacturing of apparel in the future. Second is the development of a general apparel design process model that offers a reference model for any specific apparel design process. Third is the provision of new “data mining” technology for acquiring words in human language that express affects. It should be noted that this technology is domain-independent, and thus it is applicable to any other type of product for affective design. The final contribution is the development of a method for searching apparel design parameters which describe an apparel product meeting a wearer’s required feelings described by “feeling words”. The database of words and the algorithm can be readily incorporated into commercial software for computer aided design of apparel products with the new enabler (i.e., design for affect or feeling).

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A number of efforts at NASA's Johnson Space Center are exploring ways of improving operational efficiency and effectiveness of telemetry data distribution. An important component of this is the Real-Time Data System project in the Shuttle Mission Control Center. This project's telemetry system is based on a network of engineering workstations that acquire, distribute, analyze, and display the data. Telemetry data is acquired and partially processed through a commercial programmable telemetry processor. The data is then transferred into workstations where the remaining decommutation, conversion and calibration steps are performed. The results are sent over the network to applications operating within end user workstations. This complex distributed environment is managed by PILOT, an intelligent system that monitors data flow and process integrity with the goal of providing a very high level of availability requiring minimal human involvement. PILOT is a rule-based expert system that oversees the operation of the system. It interacts with agents that operate in the local environment of each workstation and advises the local agents of system status and configuration. This enables each local agent to manage its local environment and provides a resource to which it can come with issues that need a global view for resolution. PILOT is implemented using a commercially available real-time expert system shell and operates in a heterogeneous set of hardware platforms.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / A consistent theme in spacecraft telemetry system design is the desire to obtain maximum bandwidth utilization given a fixed transmission capability (usually due to cost/weight criteria). Extensions to basic packetization telemetry architectures are discussed which can facilitate a reduction in the amount of actual data telemetered, without loss of data quality. Central to the extensions are the establishment of an "intelligent" telemetry process, which can evaluate pending data to be telemetered, and act to compress, discard, or re-formulate data before actual transmission to ground stations.

The present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment regimens in a systematic, effective and reliable way. In order to satisfy these requirements a novel approach to chemotherapy optimisation has been developed, which utilises Genetic Algorithms in an intelligent search process for good chemotherapy treatments. The following chapters consequently address various issues related to this approach. Chapter 1 gives some biomedical background to the problem of cancer and its treatment. The complexity of the cancer phenomenon, as well as the multi-variable and multi-constrained nature of chemotherapy treatment, strongly support the use of mathematical modelling for predicting and controlling the development of cancer. Some existing mathematical models, which describe the proliferation process of cancerous cells and the effect of anti-cancer drugs on this process, are presented in Chapter 2. Having mentioned the control of cancer development, the relevance of optimisation and optimal control theory becomes evident for achieving the optimal treatment outcome subject to the constraints of cancer chemotherapy. A survey of traditional optimisation methods applicable to the problem under investigation is given in Chapter 3 with the conclusion that the constraints imposed on cancer chemotherapy and general non-linearity of the optimisation functionals associated with the objectives of cancer treatment often make these methods of optimisation ineffective. Contrariwise, Genetic Algorithms (GAs), featuring the methods of evolutionary search and optimisation, have recently demonstrated in many practical situations an ability to quickly discover useful solutions to highly-constrained, irregular and discontinuous problems that have been difficult to solve by traditional optimisation methods. Chapter 4 presents the essence of Genetic Algorithms, as well as their salient features and properties, and prepares the ground for the utilisation of Genetic Algorithms for optimising cancer chemotherapy treatment. The particulars of chemotherapy optimisation using Genetic Algorithms are given in Chapter 5 and Chapter 6, which present the original work of this thesis. In Chapter 5 the optimisation problem of single-drug chemotherapy is formulated as a search task and solved by several numerical methods. The results obtained from different optimisation methods are used to assess the quality of the GA solution and the effectiveness of Genetic Algorithms as a whole. Also, in Chapter 5 a new approach to tuning GA factors is developed, whereby the optimisation performance of Genetic Algorithms can be significantly improved. This approach is based on statistical inference about the significance of GA factors and on regression analysis of the GA performance. Being less computationally intensive compared to the existing methods of GA factor adjusting, the newly developed approach often gives better tuning results. Chapter 6 deals with the optimisation of multi-drug chemotherapy, which is a more practical and challenging problem. Its practicality can be explained by oncologists' preferences to administer anti-cancer drugs in various combinations in order to better cope with the occurrence of drug resistant cells. However, the imposition of strict toxicity constraints on combining various anticancer drugs together, makes the optimisation problem of multi-drug chemotherapy very difficult to solve, especially when complex treatment objectives are considered. Nevertheless, the experimental results of Chapter 6 demonstrate that this problem is tractable to Genetic Algorithms, which are capable of finding good chemotherapeutic regimens in different treatment situations. On the basis of these results a decision has been made to encapsulate Genetic Algorithms into an independent optimisation module and to embed this module into a more general and user-oriented environment - the Oncology Workbench. The particulars of this encapsulation and embedding are also given in Chapter 6. Finally, Chapter 7 concludes the present work by summarising the contributions made to the knowledge of the subject treated and by outlining the directions for further investigations. The main contributions are: (1) a novel application of the Genetic Algorithm technique in the field of cancer chemotherapy optimisation, (2) the development of a statistical method for tuning the values of GA factors, and (3) the development of a robust and versatile optimisation utility for a clinically usable decision support system. The latter contribution of this thesis creates an opportunity to widen the application domain of Genetic Algorithms within the field of drug treatments and to allow more clinicians to benefit from utilising the GA optimisation.

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