User-Oriented Explanation for Expert Systems

In this project we are concerned with extending the range and effectiveness of explanation facilities currently available in knowledge-based expert systems by incorporating the full power of natural language techniques. As expert systems become larger and more sophisticated, it is important that they be able to communicate their reasoning effectively to their users, whether they be naive users, domain experts, or system maintainers, thus allowing them to evaluate advice the system provides, one main problem with current explanation facilities is the inability to adequately tailor an explanation to a particular user of the system. In this project, we are examining the effects of the user and discourse situation on the type of explanation provided. In particular, we are interested In developing capabilities for providing re-explanations when the user is dissatisfied with an explanation, for providing explanations in an appropriate level of detail for a given user, for adapting the explanation to the point of view the user has taken, and for using a persuasive strategy that the user is likely to accept. These efforts require the development of a semantically rich representation of the domain, capable of supporting abstractions and different points of view; the investigation of strategies that are effectively used for justification and causal explanation; and a development of the influences on level of detail, strategy selected, and point of view. We expect the results of this research to be both practical and theoretical they will improve- the range of man-machine communication currently available, making expert systems more accessible to their users, and will improve our understanding of how appropriate explanations can be produced

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

Building expert systems: cognitive emulation.

Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis

The P.R.O. expert system shell

This thesis reports the research which led to the development of the P.R .O. Expert System Shell. The P.R.O . System is primarily, though not exclusively , designed for use in ecological domains. In the light of two specific expert systems, The RCS (River Conservation Status) and the Aquaculture Systems, which were developed as part of this research, a number of areas of importance have been identified. The most significant of these is the need to handle uncertainty effectively. The style of knowledge representation to be implemented also plays an important role. After consulting the relevant literature and the available microcomputer expert system shells, a number of ideas have been included in the P.R.O. System. The P.R.O . System is a backward chaining, production system based expert system shell. It embodies a simple but effective method of handling uncertainty. An important feature of this method is that it takes cognizance of the different relative importances of the conditions which need to be satisfied before a conclusion can be reached. The knowledge base consists of more than rules and questions. It also contains meta-knowledge, which is used by the inference engine. The P.R.O. System has been designed to be of practical use. Its strongest recommendations are therefore, that the two non-trivial systems which have been implemented in it, have been accepted by the experts and their peers as systems which produce good, accurate answers .KMBT_363Adobe Acrobat 9.53 Paper Capture Plug-i

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

A report on the commercial and educational applications of expert systems

Expert, or intelligent knowledge-based, systems have emerged as the main practical application of Artificial Intelligence research. This thesis reports on their history, development and increasing commercial application. An analysis of the tasks and domains of 785 systems is reported which indicated a level of task specificity. The technology is suggestive of significant educational relevance as it is closely linked with concepts of expertise, intelligence, knowledge and learning. These basic educational concepts are discussed. The thesis reports on a survey of the use of the NCC Expert System Starter Pack in Further and Higher Education. The relationship between other computer-based learning systems and expert systems are discussed and it is argued that the development of intelligent tutoring systems is a more complex operation than the educational application of expert systems. A wide spectrum of potential educational applications is indicated. It is suggested that placing pupils in the position of knowledge engineers provides an exciting curriculum application. It is further argued that the use of expert systems in a commercial training role promises to be a major future development. Other educational applications are considered and the wider social implications associated with the use of expert systems are summarised