Application of artificial intelligence (Al) has been a topic of interest among researchers for the past decade or more. Years of research in the commercial application of Al, availability of hardware support for Al application and affordability of software and hardware has generated a lot of interest in this field and brought this technology within the reach of micro-computer based users. The commercial impact of AI is due to expert systems (ESs). ES technology is a collection of methods and techniques for constructing human-machine systems with specialized problem solving expertise. This project explores the application of ESs in landscape architecture by developing a prototype ES and testing implications of its use with designers while working on a hypothetical problem in a studio environment. The development process helps identify the typical difficulties of such an application, to uncover technical problems, and to identify areas needing further research. The project aims at building an ES that provides very limited preliminary data and design guidelines to initialize the design process and keeps track of the most fundamental issues necessary for planning, thus acting as an expert and assistant simultaneously. The idea is to explore the possibility of applying ESs to facilitate the design process so that designers may concentrate on other important aspects of design which include intuitive judgement about qualitative aspects. / Master of Landscape Architecture

This study proposes an Architectural Expert System (AES) to act as a design partner for architectural designers. Architectural designers are faced with a very complex task of searching a solution space, which is a labyrinth of several domains ranging from social to cultural, and from aesthetic to scientific. With the number of domains come a number of experts of that domain. After progressing through tedious analytical procedures involving the physical principles in architecture, and applying the knowledge of experience, the experts are able to convert the raw data into useful design guidelines. Research in the field of artificial intelligence has developed techniques which can capture such expertise in a computer program, which then emulates the expert. This technology is know as Expert System (ES). This study has used this technology to develop a system to aid architectural design. An AES model is derived from literature review. As the nature of a system based on this model is complex and would require custom built software, an alternative is developed based on the derived model. Based on this alternative, a prototype is developed for energy audit and energy conservation by capturing the expertise of an energy conscious design expert. This prototype module is one component of the sub-system of AES and provides an example for further modules. Various areas such as design, architecture, artificial intelligence and expert systems technology, and energy conscious design and energy conservation converge, and become parts of this study. / Master of Science

No / Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that "five toxicologists may have six opinions". In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation of Weight of Evidence approaches and in silico modelling proposed by REACH, there is a higher appeal of numerical quality values than nominal (categorical) ones, where the proposed fuzzy expert system could help. Most importantly, the deriving processes of quality values generated in this way are fully transparent, and thus comprehensible, for final users, which is another vital point for policy making specified in REACH. Case studies have been conducted and this report not only shows the promise of the approach, but also demonstrates the difficulties of the approach and thus indicates areas for future development. / U 7th Framework Programme Integrated Project “Integrated In Silico Models for Prediction of Human Repeated Dose Toxicity of Cosmetics to Optimise Safety” (COSMOS). Grant Number: 266835. Cosmetics Europe.

Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed. Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis. Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed. In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

This research report investigates the use of an expert system to aid project engineers at the Naval Training Systems Center in making decisions concerning the requirements of the computer systems used in simulators. For a prototype system domain, the author chose an expert system that would generate a software development cost estimate. This system questions the user about the features and options required on the training system. The expert system then analyzes the information to generate a “lines of code” estimate. A selected model will combine various factors to generate s value answer for the user. The capabilities and features of current expert system development tools are reviewed as to what features would best address this problem domain. EXSYS, a rule-based expert system shell that runs on both Zenith and IBM PCs, was selected to develop the prototype because of its capability to meet the requirements of the software cost estimation domain. The COCOMO estimation model was selected to generate the user answers. The technique of using a rule-based system in combination with other management decision tools, such as spreadsheets, holds a potential of being an excellent approach for providing a tool for storing and utilizing estimation data and heuristics.

This report discusses the major functions, decisions, and strategies of automated storage and retrieval systems (AR/RS). The report surveys the essential features of expert systems and discusses how they can be applied in automated warehousing environments. A blackboard expert system architecture was examined and found to be a flexible and responsive control system for automated warehousing applications. A simple AR/RS expert system was constructed using an expert system software package. A warehousing simulation was performed which compared the expert system’s performance to a typical AR/RS control system. The expert system produced increased efficiency of operation because of the intelligent rules programmed into the system.

An expert system approach to help OR naive users formulate and solve goal programs is proposed. The approach is demonstrated for single product blending problems using VP-Expert as the developmental tool. Results of a study using undergraduate and graduate business students to test the expert system effectiveness are provided. An expert system determines the problem type using a taxonomy based upon problem context. Each problem type possesses distinct characteristics. Characteristics of twenty-four different problem types are discussed. Formulation of constraints using problem characteristics is demonstrated. The expert system uses constraint information to assist users in goal selection. Goal structures are constructed using a pairwise comparison technique. Solution values, recommendations based upon sensitivity analysis, and trade-offs between conflicting goals are provided to the user. A feedback loop permitting model changes and reiteration of solution and recommendation steps is provided. / Ph. D.

A knowledge-based system is formulated to guide the search strategy selection process in simulation optimization. This system includes a framework for machine learning which enhances the knowledge base and thereby improves the ability of the system to guide optimizations. Response surfaces (i.e., the response of a simulation model to all possible input combinations) are first classified based on estimates of various surface characteristics. Then heuristics are applied to choose the most appropriate search strategy. As the search is carried out and more information about the surface becomes available, the knowledge-based system reclassifies the response surface and, if appropriate, selects a different search strategy. Periodically the system’s Learner is invoked to upgrade the knowledge base. Specifically, judgments are made to improve the heuristic knowledge (rules) in the knowledge base (i.e., rules are added, modified, or combined). The Learner makes these judgments using information from two sources. The first source is past experience -- all the information generated during previous simulation optimizations. The second source is results of experiments that the Learner performs to test hypotheses regarding rules in the knowledge base. The great benefits of simulation optimization (coupled with the high cost) have highlighted the need for efficient algorithms to guide the selection of search strategies. Earlier work in simulation optimization has led to the development of different search strategies for finding optimal-response-producing input levels. These strategies include response surface methodology, simulated annealing, random search, genetic algorithms, and single-factor search. Depending on the characteristics of the response surface (e.g., presence or absence of local optima, number of inputs, variance), some strategies can be more efficient and effective than others at finding an optimal solution. If the response surface were perfectly characterized, the most appropriate search strategy could, ideally, be immediately selected. However, characterization of the surface itself requires simulation runs. The knowledge-based system formulated here provides an effective approach to guiding search strategy selection in simulation optimization. / Ph. D.

The increasing effort to develop auditing expert systems raises many questions about their impact on public accounting firms. This research examines the status of expert systems in auditing and investigates the possible future impacts of expert systems on auditing firms. The research involved two separate studies. First, a Delphi study involving auditing and expert-systems experts investigated the likelihood of the proposed future impacts of expert systems on auditing firms. The purpose of the Delphi study was not only to identify the most and least likely impacts, but also to explore the reasons why respondents felt they were the most or least likely. The Delphi panel suggests that expert systems will very likely have an impact on auditing firms in the next decade. The most likely impact identified was that use of an expert system for an audit task provides documentation references for audit judgements and reasoning. other specific impacts that were identified as very likely include distribution of expertise, increased ability to handle complex analyses, and improved decision consistency and quality. The panel also indicated that use of expert systems in auditing is very likely to impact the education of auditors. Second, a case study of an auditing firm using an audit planning expert system provided evidence concerning the impact of an expert system in use. The case study confirmed that, even today, expert systems may be used to provide documentation references and enhance decision consistency and quality. In the situation studied, the impacts were most evident for the less experienced users. The primary contribution of the research is to address questions and concerns about the impact of expert systems on the aUditing profession. The pool of potential impacts of expert systems that has been discussed in the literature can now be narrowed to focus on the most likely impacts. This research is the first step in developing a theory of expert systems impacts. It provides (1) the impetus for further research addressing more specific areas of potential expert systems impact and (2) case study evidence about expert systems impacts that are occurring today. Reasons for the most probable impacts of expert systems on auditing in the future are identified. / Ph. D.

The CODER (COmposite Document Expert/Extended/Effective Retrieval) project is a multi-year effort to investigate how best to apply artificial intelligence methods to increase the effectiveness of information retrieval systems. Particular attention is being given to analysis and representation of heterogeneous documents, such as electronic mail digests or messages, which vary widely in style, length, topic, and structure. In order to ensure system adaptability and to allow reconfiguration for controlled experimentation, the project has been designed as a moderated expert system. This thesis covers the design problems involved in providing a unified architecture and knowledge representation scheme for such a system, and the solutions chosen for CODER. An overall object-oriented environment is constructed using a set of message-passing primitives based on a modified Prolog call paradigm. Within this environment is embedded the skeleton of a flexible expert system, where task decomposition is performed in a knowledge-oriented fashion and where subtask managers are implemented as members of a community of experts. A three-level knowledge representation formalism of elementary data types, frames, and relations is provided, and can be used to construct knowledge structures such as terms, meaning structures, and document interpretations. The use of individually tailored specialist experts coupled with standardized blackboard modules for communication and control and external knowledge bases for maintenance of factual world knowledge allows for quick prototyping, incremental development, and flexibility under change. The system as a whole is structured as a set of communicating modules, defined functionally and implemented under UNIX™ using sockets and the TCP/IP protocol for communication. Inferential modules are being coded in MU-Prolog; non-inferential modules are being prototyped in MU-Prolog and will be re-implemented as needed in C++. / M.S.