Over the past decade, two types of decision aids, i.e., decision support systems (DSS) and expert systems (ES), have been developed along parallel paths, showing some significant differences in their software architectures, capabilities, limitations, and other characteristics. The synergy of DSS and ES, however, has great potential for helping make possible a generalized approach to developing a decision aid that is powerful, intelligent, and friendly. This research establishes a framework for such decision aids in order to determine the elementary components and their interactions. Based on this framework, a generalized intelligent problem solving system (GIPSS) is deveolped as a decision aid generator. A relational model is designed to provide a unified logical view of each type of knowledge including factual data, modeling knowledge, and heuristic rules. In this knowledge model, a currently existing relational DBMS, with some extension, is utilized to manage each type of knowledge. For this purpose a relational resolution inference mechanism has been devised. A prototype GIPSS has been developed based on this framework. Two domain specific decision aids, COCOMO which estimates software development effort and cost, and CAPO which finds optimal process organization, have been implemented by using the GIPSS as a decision aid generator, demonstrating such features as its dynamic modeling capabilities and learning capabilities.

Knowledge acquisition is not only the most important but also most difficult task knowledge engineers face when they begin to develop expert systems. One of the first problems they encounter is the need to identify at least one individual with appropriate expertise who is able and willing to participate in the development project. They must also be able to use a variety of techniques to elicit the knowledge that they require. These include such traditional knowledge acquisition methods as interviewing, thinking-aloud protocol analysis, on-site observation, and repertory grid analysis. As expert system applications have become more complex, knowledge engineers have found that they must work with and tap the domain knowledge of not one but several individuals. They have also discovered that the traditional methods do not work well in eliciting the knowledge residing in a group of individuals. The complexity of the systems, the difficulties inherent in working with multiple experts, and the lack of appropriate tools have combined to make the knowledge acquisition task even more arduous and time consuming. Group Decision Support Systems (GDSS) have been proven to be useful tools for improving the efficiency and effectiveness of a multiplicity of group activities. It would appear that by bringing experts together in a GDSS environment and using computer-based tools to facilitate group interaction and information exchange, a knowledge engineer could eliminate many of these problems. This research was designed to explore the possibility of using a GDSS environment to facilitate knowledge acquisition from multiple experts. The primary research question was "Does A GDSS environment facilitate the acquisition of knowledge from multiple experts?" The principle contributions of this research are (1) demonstration of the first use of a GDSS environment to elicit knowledge from multiple experts; (2) establishment of a methodology for knowledge acquisition in a GDSS environment; (3) development of process models for acquiring knowledge; (4) development of guidelines for designing and evaluating group support tools; and (5) recognition of some implications of using a computer-supported cooperative approach to extract knowledge from a group of experts. (Abstract shortened with permission of author.)

This report addresses the research applied towards the automation of the engineering design process, in particular the structural design process. The three important stages of the structural design process are: the specifications, preliminary design and the detailed design. An iterative redesign architecture of the structural design process lends itself to automation. The automation of the structural design can improve both the cost and the reliability, and enhance the productivity of the human designers. To the extent that the assumptions involved in the design process are explicitly represented and automatically inforced, the design errors resulting from the violated assumptions can be avoided. Artificial Intelligence (AI) addresses the automation of complex and knowledge-intensive tasks such as the structural design process. It involves the development of the Knowledge Based Expert System (KBES). There are several tools, also known as expert shells, and languages available for the development of knowledge-based expert systems. A general purpose language, called LISP, is very popular among researchers in AI and is used as an environmental tool for the development of the KBES for the structural design process. The resulting system, called Expert-SEISD, is very generic in nature. The Expert-SEISD is composed of the user interface, inference engine, domain specific knowledge and data bases and the knowledge acquisition. The present domain of the Expert-SEISD encompasses the design of structural components such as beams and plates. The knowledge acquisition module is developed to facilitate the incorporation of new capabilities (knowledge or data) for beams, plates and for new structural components. The decision making is an integral part of any design process. A decision-making model suitable for the specifications extraction and the preliminary design phases of the structural design process is proposed and developed based on the theory of fuzzy sets. The methods developed here are evaluated and compared with similar methods available in the literature. The new method, based on the union of fuzzy sets and contrast intensification, was found suitable for the proposed model. It was implemented as a separate module in the Expert-SEISD. A session with the Expert-SEISD is presented to demonstrate its capabilities of beam and plate designs and knowledge acquisition.

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In many settings, agents engage in problem-solving activities, which require them to share resources, act on each others behalf, coordinate individual acts, etc. If autonomous agents are to e ectively interact (or support interaction among humans) in situations such as deciding whom and how to approach the provision of a resource or the performance of an action, there are a number of important questions to address. Who do I choose to delegate a task to? What do I need to say to convince him/her to do something? Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? Research in argumentation strategies has received signi cant attention in recent years, and a number of approaches has been proposed to enable agents to reason about arguments to present in order to persuade another. However, current approaches do not adequately address situations where agents may be operating under social constraints (e.g., policies) that regulate behaviour in a society. In this thesis, we propose a novel combination of techniques that takes into consideration the policies that others may be operating with. First, we present an approach where evidence derived from dialogue is utilised to learn the policies of others. We show that this approach enables agents to build more accurate and stable models of others more rapidly. Secondly, we present an agent decision-making mechanism where models of others are used to guide future argumentation strategy. This approach takes into account the learned policy constraints of others, the cost of revealing in- formation, and anticipated resource availability in deciding whom to approach. We empirically evaluate our approach within a simulated multi-agent frame- work, and demonstrate that through the use of informed strategies agents can improve their performance.

The thesis investigates two domains, initially the kite domain and then part of a more demanding Rolls-Royce domain (jet engine design). Four main types of refinement rules that use the associated application conditions and domain ontology to support the maintenance of constraints are proposed. The refinement rules have been implemented in ConEditor and the extended system is known as ConEditor+. With the help of ConEditor+, the thesis demonstrates that an explicit representation of application conditions together with the corresponding constraints and the domain ontology can be used to detect inconsistencies, redundancy, subsumption and fusion, reduce the number of spurious inconsistencies and prevent the identification of inappropriate refinements of redundancy, subsumption and fusion between pairs of constraints.

When a tropical cyclone (TC) is within 360 nautical miles of Kadena AB, the Air Force's Typhoon Determination (TYDET) program is used to estimate TC-induced winds expected at the base. Best-track data and Joint Typhoon Warning Center (JTWC) forecasts are used to evaluate systematic errors in TYDET. The largest contributors to errors in TYDET are a systematic error by which wind speeds are too large and the lack of size and symmetry parameters. To examine these parameters, best-track and forecasts are used to classify TCs as small or large and symmetric or asymmetric. A linear regression technique is then used to adjust TYDET forecasts based on the best-track and forecast position, size, and symmetry categories. Using independent data, over 65 percent of the overall cross-wind forecasts were improved and more than 60 percent of the cross-wind forecasts were improved when verifying conditions noted a cross-wind of 20 knots or greater. The effectiveness of the corrections and implications for TYDET forecasts are examined in relation to errors in forecast data used to initialize TYDET. A similar approach as developed here for the TYDET model at Kadena AB is proposed for other bases within the Pacific theater.

D.Ing. (Electrical and Electronic Engineering) / The electricity shortage in South Africa has required the implementation of demand side management (DSM) projects. The DSM projects were implemented by installing energy monitoring and control systems to monitor certain mining aspects such as water pumping systems. Certain energy saving procedures and control systems followed by the mining industry are not sustainable and must be updated regularly in order to meet any changes in the water pumping system. In addition, the present water pumping, monitoring, and control system does not predict the energy consumption or the underground water dam levels. Hence, there is a need to introduce new monitoring system that could control and predict the energy consumption of the underground water pumping system and dam levels based on present and historical data. The work is undertaken to investigate the feasibility of using artificial intelligence in certain aspects of the mining industry. If successful, artificial intelligence systems could lead to improved safety and reduced electrical energy consumption, and decreased human error that could occur throughout the pump station monitoring and control process ...

The purpose of this study was to explore the subject of timescale estimating for rule-based systems. A model for estimating the timescale necessary to build rule-based systems was built and then tested in a controlled environment.

The primary purpose of the study was to develop an expert system that could C D perform medical diagnoses In selected problem areas, and C2) provide diagnostic Insights to assist medical students In their training. An expert system Is a computer-based set of procedures and algorithms that can solve problems In a given domain. Two research questions were proposed. The first was "Given a problem space defined by a matrix of diseases and symptoms, can a computer-based model be derived that will consistently perform accurate and efficient diagnoses of cases within that problem area?" The second question was "If the techniques derived from the model are taught to a medical student, is there a subsequent improvement of diagnostic skill?" An expert system was developed which met the objectives of the study. It was able to diagnose cases in the two problem areas studied with an accuracy of 94-95%. Furthermore, it was able to perform those diagnoses in a very efficient manner, often using no more than the theoretical minimum number of steps. The expert system employed three phases: rapid search by discrimination, confirmation by pattern matching against prototypes, and elimination of some candidates (impossible states) by making use of negative information. The discrimination phase alone achieved accuracies of 73-78%. By comparison, medical students achieved mean accuracies of 54-55% in the same problem areas. This suggests that novices could improve their diagnostic accuracy by approximately 20% by following the simple rules used in the first phase of the expert system. Curricular implications are discussed. When 49 first-year medical students at the Texas College of Osteopathic Medicine were exposed to some of the insights of the expert system by means of a videotaped 10- minute lecture, their diagnostic approach was modified and the accuracy of their diagnoses did improve. However, the degree of Improvement was not statistically significant. Recommendations for further research are made.