STRUCTURING KNOWLEDGE ACQUISITION THROUGH GENERIC TASKS: A CASE STUDY IN HINDSIGHT

Knowledge Acquisition is widely recognized as the single major bottleneck in the commercialization of Expert Systems technology. The typically ad-hoc choice of techniques for eliciting and representing expert knowledge, makes Expert Systems development expensive and prone to failure. Arguments have been made in the Knowledge Acquisition literature for performing an epistemological or "knowledge-level" analysis to "structure" the knowledge elicitation process. The need of the hour is for an empirical evaluation of these claims. In this paper, we present the results of a study that evaluates an approach to Structured Knowledge Acquisition, that is based on analyzing expert behavior using generic problem-solving tasks. Data from a large Expert Systems project currently nearing completion, has been used for the study.Information Systems Working Papers Serie

Genisa: A web-based interactive learning environment for teaching simulation modelling

Intelligent Tutoring Systems (ITS) provide students with adaptive instruction and can facilitate the acquisition of problem solving skills in an interactive environment. This paper discusses the role of pedagogical strategies that have been implemented to facilitate the development of simulation modelling knowledge. The learning environment integrates case-based reasoning with interactive tools to guide tutorial remediation. The evaluation of the system shows that the model for pedagogical activities is a useful method for providing efficient simulation modelling instruction

Facilitating teacher participation in intelligent computer tutor design : tools and design methods.

This work addresses the widening gap between research in intelligent tutoring systems (ITSs) and practical use of this technology by the educational community. In order to ensure that ITSs are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools that facilitate rapid prototyping and testing of curriculum, and are tailored for usability by teachers. The system (called KAFITS) also serves as a test-bed for experimentation with multiple tutoring strategies. The design includes novel methodologies for tutoring strategy representation (Parameterized Action Networks) and overlay student modeling (a layered student model), and incorporates considerations from instructional design theory. It also allows for considerable student control over the content and style of the information presented. Highly interactive graphics-based tools were built to facilitate design, inspection, and modification of curriculum and tutoring strategies, and to monitor the progress of the tutoring session. Evaluation of the system includes a sixteen-month case study of three educators (one being the domain expert) using the system to build a tutor for statics (forty topics representing about four hours of on-line instruction), testing the tutor on a dozen students, and using test results to iteratively improve the tutor. Detailed throughput analysis indicates that the amount of effort to build the statics tutor was, surprisingly, comparable to similar figures for building (non-intelligent) conventional computer aided instructional systems. Few ITS projects focus on educator participation and this work is the first to empirically study knowledge acquisition for ITSs. Results of the study also include: a recommended design process for building ITSs with educator participation; guidelines for training educators; recommendations for conducting knowledge acquisition sessions; and design tradeoffs for knowledge representation architectures and knowledge acquisition interfaces

ARC-TEC : acquisition, representation and compilation of technical knowledge

A global description of an expert system shell for the domain of mechanical engineering is presented. The ARC-TEC project constitutes an AI approach to realize the CIM idea. Along with conceptual solutions, it provides a continuous sequence of software tools for the acquisition, representation and compilation of technical knowledge. The shell combines the KADS knowledge-acquisition methodology, the KL-ONE representation theory and the WAM compilation technology. For its evaluation a prototypical expert system for production planning is developed. A central part of the system is a knowledge base formalizing the relevant aspects of common sense in mechanical engineering. Thus, ARC-TEC is less general than the CYC project but broader than specific expert systems for planning or diagnosis

Research Proposal: Preference Acquisition through Reconciliation of Inconsistencies

The quality of performance of a decision-support system (or an expert system) is determined to a large extent by its underlying preference model (or knowledge base). The difficulties in preference and knowledge acquisition make them a major focus of current research in decision-support and expert systems. Researchers have used various concepts to develop promising acquisition techniques. One of the concepts used is knowledge maintenence where the knowledge base is changed in response to incorrect or inadequate performance by the expert system. This dissertation investigates a preference acquisition technique based on the reconciliation of inconsistencies between the preference model and the decision maker by allowing the decision maker to modify the preference model interactively. The technique can be used in the class of decision-support systems which objectively evaluate competing plans and select the best plan. The technique will be implemented in the domain of evaluating three-dimensional (3-D) radiation treatment plans. Another major aim of the dissertation is to develop a clinically-relevant objective plan-evaluation model for 3-D radiation treatment plans, and to build a clinical decision-support system to assist in that task using the new preference acquisition method

Visualisation Tools for Multi-Perspective, Cross-Sector, Long-Term Infrastructure Performance Evaluation

Across different infrastructure sectors there are systems that help to monitor the current and near-future operation and performance of a particular system. Whilst Supervisory Control and Data Acquisition (SCADA) systems are critical to maintaining acceptable levels of functionality, they do not provide insights over the longer timescales across which strategic investment decisions play out. To understand how individual or multiple, interdependent, infrastructure sectors perform over longer timescales, capacity/demand modelling is required. However, the outputs of such models are often a complex high-dimensionality result-set, and this complexity is further compounded when crosssector evaluation is required. To maximise utility of such models, tools are required that can process and present key outputs. In this paper we describe the development of prototype tools for infrastructure performance evaluation in relation to different strategic decisions and the complex outputs generated from capacity and demand models of five infrastructure sectors (energy, water, waste water, solid waste, transport) investigated within the UK Infrastructure Transitions Research Consortium (ITRC). By constructing tools that expose various dimensions of the model outputs, a user is able to take greater control over the knowledge discovery process

Mprolog as an expert system development tool

The difficult process of designing expert systems has caused the development of many useful expert system design tools. A new tool, called MPROLOG, has been developed into an expert system tool by giving the designer the power of a programming language, PROLOG, along with a method for specifying uncertainties. Descriptions of KEE and ART, two popular expert system design tools, and MPROLOG are presented along with a description of perhaps the most important phase of expert system design: knowledge acquisition. An analysis of the implementation of an MPROLOG expert system, the F-111 Wing Commander, throughout the knowledge acquisition and design phases is also documented. Finally, an evaluation of MPROLOG as an expert system design tool is presented

Semantic Models as Knowledge Repositories for Data Modellers in the Financial Industry

Data modellers working in the financial industry are expected to use both technical and business knowledge to transform data into the information required to meet regulatory reporting requirements. This dissertation explores the role that semantic models such as ontologies and concept maps can play in the acquisition of financial and regulatory concepts by data modellers. While there is widespread use of semantic models in the financial industry to specify how information is exchanged between IT systems, there is limited use of these models as knowledge repositories. The objective of this research is to evaluate the use of a semantic model based knowledge repository using a combination of interviews, model implementation and experimental evaluation. A semantic model implementation is undertaken to represent the knowledge required to understand sample banking regulatory reports. An iterative process of semantic modelling and knowledge acquisition is followed to create a representation of technical and business domain knowledge in the repository. The completed repository is made up of three concept maps hyper-linked to an ontology. An experimental evaluation of the usefulness of the repository is made by asking both expert and novice financial data modellers to answer questions that required both banking knowledge and an understating of the information in regulatory reports. The research suggests that both novice and expert data modellers found the knowledge in the ontology and concept maps to be accessible, effective and useful. The combination of model types allowing for variations in individual styles of knowledge acquisition. The research suggests that the financial trend in the financial industry for semantic models and ontologies would benefit from knowledge management and modelling techniques

A framework and computer system for knowledge-level acquisition, representation, and reasoning with process knowledge

The development of knowledge-based systems is usually approached through the combined skills of software and knowledge engineers (SEs and KEs, respectively) and of subject matter experts (SMEs). One of the most critical steps in this task aims at transferring knowledge from SMEs’ expertise to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this process is costly and error prone. Alleviating such knowledge acquisition bottleneck requires enabling SMEs with the means to produce the target knowledge representations, minimizing the intervention of KEs. This is especially difficult in the case of complex knowledge types like processes. The analysis of scientific domains like Biology, Chemistry, and Physics uncovers: (i) that process knowledge is the single most frequent type of knowledge occurring in such domains and (ii) specific solutions need to be devised in order to allow SMEs to represent it in a computational form. We present a framework and computer system for the acquisition and representation of process knowledge in scientific domains by SMEs. We propose methods and techniques to enable SMEs to acquire process knowledge from the domains, to formally represent it, and to reason about it. We have developed an abstract process metamodel and a library of problem solving methods (PSMs), which support these tasks, respectively providing the terminology for SME-tailored process diagrams and an abstract formalization of the strategies needed for reasoning about processes. We have implemented this approach as part of the DarkMatter system and formally evaluated it in the context of the intermediate evaluation of Project Halo, an initiative aiming at the creation of question answering systems by SMEs