A methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

The aim of this thesis is to develop a methodology for the selection of a paradigm of reasoning under uncertainty for the expert system developer. This is important since practical information on how to select a paradigm of reasoning under uncertainty is not generally available. The thesis explores the role of uncertainty in an expert system and considers the process of reasoning under uncertainty. The possible sources of uncertainty are investigated and prove to be crucial to some aspects of the methodology. A variety of Uncertainty Management Techniques (UMTs) are considered, including numeric, symbolic and hybrid methods. Considerably more information is found in the literature on numeric methods, than the latter two. Methods that have been proposed for comparing UMTs are studied and comparisons reported in the literature are summarised. Again this concentrates on numeric methods, since there is more literature available. The requirements of a methodology for the selection of a UMT are considered. A manual approach to the selection process is developed. The possibility of extending the boundaries of knowledge stored in the expert system by including meta-data to describe the handling of uncertainty in an expert system is then considered. This is followed by suggestions taken from the literature for automating the process of selection. Finally consideration is given to whether the objectives of the research have been met and recommendations are made for the next stage in researching a methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

Reasoning with uncertainty using Nilsson's probabilistic logic and the maximum entropy formalism

An expert system must reason with certain and uncertain information. This thesis is concerned with the process of Reasoning with Uncertainty. Nilsson's elegant model of "Probabilistic Logic" has been chosen as the framework for this investigation, and the information theoretical aspect of the maximum entropy formalism as the inference engine. These two formalisms, although semantically compelling, offer major complexity problems to the implementor. Probabilistic Logic models the complete uncertainty space, and the maximum entropy formalism finds the least commitment probability distribution within the uncertainty space. The main finding in this thesis is that Nilsson's Probabilistic Logic can be successfully developed beyond the structure proposed by Nilsson. Some deficiencies in Nilsson's model have been uncovered in the area of probabilistic representation, making Probabilistic Logic less powerful than Bayesian Inference techniques. These deficiencies are examined and a new model of entailment is presented which overcomes these problems, allowing Probabilistic Logic the full representational power of Bayesian Inferencing. The new model also preserves an important extension which Nilsson's Probabilistic Logic has over Bayesian Inference: the ability to use uncertain evidence. Traditionally, the probabilistic, solution proposed by the maximum entropy formalism is arrived at by solving non-linear simultaneous equations for the aggregate factors of the non- linear terms. In the new model the maximum entropy algorithms are shown to have the highly desirable property of tractability. Although these problems have been solved for probabilistic entailment the problems of complexity are still prevalent in large databases of expert rules. This thesis also considers the use of heuristics and meta level reasoning in a complex knowledge base. Finally, a description of an expert system using these techniques is given

Expert systems for foundation design

Expert system technology has been brought from Artificial Intelligence research laboratories to the real world over the last decade. However, to date, there are few expert systems that have been developed for foundation design work. The problems are due to technical and psychological factors and are similiar to those when computers were firstly introduced. In this thesis, the difficulties of building expert systems for foundation engineering application are identified. The work in this thesis is an attempt to study the applicability of expert systems to foundation design and find solutions to existing difficulties. The thesis explores ways in which geotechnical engineers can be persuaded to accept the technology and develop their own systems, or to use developed systems to assist their work. Features of conventional expert systems are investigated, modified and improved such that the developed systems are more suitable for foundation design work and engineers may have more confidence in developing systems or using the developed systems. Three ways of building expert systems are studied and compared in terms of flexibility, user and developer-friendliness, user-confidence, and validation of the developed system. The three ways involve: i) using Turbo PROLOG to encode the system from ‘scratch’, ii) using a development tool (also termed a shell in this thesis), and iii) using a spreadsheet. A new shell is specifically designed and developed to facilitate the second approach. Examples of systems for geotechnical application using each approach are described in detail in this thesis

A probabilistic reasoning and learning system based on Bayesian belief networks

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Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

An artificial intelligence framework for experimental design and analysis in discrete event simulation

Simulation studies cycle through the phases of formulation, programming, verification and validation, experimental design and analysis, and implementation. The work presented has been concerned with developing methods to enhance the practice and support for the experimental design and analysis phase of a study. The investigation focussed on the introduction of Artificial Intelligence (AI) techniques to this phase, where previously there existed little support. The reason for this approach was the realisation that the experimentation process in a simulation study can be broken down into a reasoning component and a control of execution component. In most studies, a user would perform both of these. The involvement of a reasoning process attracted the notion of artificial intelligence or at least the prospective use of its techniques. After a study into the current state of the art, work began by considering the development of a support system for experimental design and analysis that had human intelligence and machine control of execution. This provided a semi-structured decision-making environment in the form of a controller that requested human input. The controller was made intelligent when it was linked to a non-procedural (PROLOG) program that provided remote intelligent input from either the user or default heuristics. The intelligent controller was found to enhance simulation experimentation because it ensures that all the steps in the experimental design and analysis phase take place and receive appropriate input. The next stage was to adopt the view that simulation experimental design and analysis may be enhanced through a system that had machine intelligence but expected human control of execution. This provided the framework of an advisor that adopted a consultation expert system paradigm. Users were advised on how to perform simulation experimentation. Default reasoning strategies were implemented to provide the system with advisory capabilities in the tasks of prediction, evaluation, comparison, sensitivity analysis, transient behaviour, functional relations, optimisation. Later the controller and the advisor were linked to provide an integrated system with both machine intelligence and machine control of execution. User involvement in the experimentation process was reduced considerably as support -¿as provided in both the reasoning and control of execution aspects. Additionally, this integrated system supports facilities for refinement purposes that aim at turning the system’s knowledge into expertise. It became theoretically possible for other simulation experts to teach the system or experiment with their own rules and knowledge. The following stage considered making the knowledge of the system available to the user, thereby turning the system into a teacher and providing pedagogical support Teaching was introduced through explanation and demonstration. The explanation facility used a mixed approach: it combined a first time response explanation facility to "how" and "why" questions with a menu driven information system facility for "explain" requests or further queries. The demonstration facility offers tutorials on the use of the system and how to carry out an investigation of any of the tasks that the system can address. The final part of the research was to collect some empirical results about the performance of the system. Some experiments were performed retroactively on existing studies. The system was also linked to a data-driven simulation package 'hat permitted evaluation using some large scale industrial applications. The system’s performance was measured by its ability to perform as well as students with simulation knowledge but not necessarily expertise. The system was also found to assist the user with little or no simulation knowledge to perform as well as students with knowledge. This study represents the first practical attempts to use the expert system framework to model the processes involved in simulation experimentation. The framework described in this thesis has been implemented as a prototype advisory system called WES (Warwick Expert Simulator). The thesis concludes that the framework proposed is robust for this purpose

Prolog and expert systems

The first part of the thesis provides an introduction to the logic programming language Prolog and some areas of current research. The use of compilation to make Prolog faster and more efficient is studied and a modified representation for complex structures is presented. Two programming tools are also presented. The second part of the thesis focuses on one problem which arises when implementing an Expert System using Prolog. A practical three-valued Prolog implementation is described. An interpreter accepts three-valued formulae and converts these into a Prolog representation. Formulae are in clausal form which allows disjunctive conclusions to rules. True and false formulae are stated explicitly and therefore the interpreter is able to perform useful consistency checks when information is added to the data base

The treatment of uncertainty in construction price modelling

The purpose of this thesis was to acquaint the reader on the nature of the uncertainty present in construction price forecasting and to introduce an environment that has the ability to integrate these uncertainties with greater consistency than that possessed by available price models. The objective of this thesis was to establish that uncertainty can be explicitly treated in price forecasting models. This would have two benefits to concerned parties. Firstly, the effectiveness of price forecasts could be improved as provision could be made for any uncertain variable. This will be of great benefit to the client, as a more accurate assessment of the building process could be established at an earlier stage of the design process. Secondly, the price forecast will be more useful to quantity surveyors, architects and clients, as it would 'quantify' the extent of the uncertainty which could be provided for in a more meaningful manner. In order to establish that existing price models do not deal with the uncertainty present at the time of forecasting, the price models used by practitioners were evaluated against the different types of uncertainty found at the different stages of the price forecasting process. Once this had been established, eight techniques that have the ability to treat various forms of uncertainty, were presented. After analysing the techniques abilities to cope with the uncertainties associated with price forecasting, it was established that certain of these techniques do have the ability, and are suitable, to be incorporated into the price forecasting process. From the results of a questionnaire survey conducted on quantity surveying offices in South Africa, it was found that the price models used by practitioners do not take uncertainty into account, and have in fact, the potential for uncertainty inducement. Some of the uncertainty found to be present in the preparation of a construction price forecast include the lack or incompleteness of design information, the uncertainty in the communication of design information, the variability in the data used by quantity surveyors and, the uncertainty in the choice of price model during the different stages of the design process. As a possible solution to the problem of uncertainty, an expert system environment, utilising a three-dimensional classification of uncertainty, has been proposed. It has been proved that this environment has the ability to cater for the uncertainty associated with the price forecasting process, as well as having the attribute of providing the user with the reasoning behind the logic that the expert system has followed, a characteristic not possible with the traditional forms of price models. From the findings of this thesis, it can be concluded that the methods of price modelling used by quantity surveying practitioners, are unable to take uncertainty into account effectively. It can also be concluded that an expert system environment has the ability to handle the different forms of uncertainty found at the various stages of construction design. The proposed model is conceptual in nature and has not been tested in practice. It is therefore recommended that further research be carried out in this field, with the aim of producing a construction price forecasting expert system which utilises the proposed three-dimensional classification of uncertainty