A Cognitive Model and Computer Tutor for Programming Recursion

This article discusses cognitive models of learning to program recursion and their relation to lessons on recursion in an intelligent computer tutor for LISP programming (the LISP Tutor). The cognitive models are implemented as production systems in which programming skill is characterized as the decomposition of programming goals into subgoals and elementary actions via the application of programming plans. Two sets of learning mechanisms are used in the cognitive models. Analogical problem-solving mechanisms use declarative knowledge of example program solutions to overcome problem-solving impasses. Knowledge compilation mechanisms summarize problem solutions into efficient problem-solving skill. Analyses and simulations of novice and expert programming were used to develop ideal models of the programming knowledge to confer upon students and bugs that characterize common misconceptions. The LISP Tutor uses the ideal models and bugs to guide its interactions with students. Experimental evaluations of the LISP Tutor indicate that it is more efficient and effective than classroom instruction.     

Empirical Studies of Programming Knowledge

We suggest that expert programmers have and use two types of programming knowledge: 1) programming plans, which are generic program fragments that represent stereotypic action sequences in programming, and 2) rules of programming discourse, which capture the conventions in programming and govern the composition of the plans into programs. We report here on two empirical studies that attempt to evaluate the above hypothesis. Results from these studies do in fact support our claim.     

On making expert systems more like experts

Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem-solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.     

Using programming expertise for controlling software synthesis

Achieving efficient software implementations requires a great deal of knowledge, intelligence, and expertise on the part of programmers. One way to enhance software productivity is to incorporate the knowledge and skills of expert programmers into software synthesis systems to automate software development processes. Although many software synthesis systems have been developed, automatic control of synthesis remains a difficult problem. Understanding the role of expertise in software synthesis, and making it more explicit, can help us not only to gain autonomy in controlling the synthesis processes but also to better justify the design, implementations, selection of data structures or algorithms employed in constructing code. Our project aims at making synthesis as autonomous as possible by advances in intelligent control mechanisms to reduce user interaction in the synthesizer. In our earlier work, a blackboard control framework for controlling synthesis processes was introduced. This paper describes how the control framework language was designed and how knowledge in the knowledge bases of the framework was acquired and constructed. We present an example that shows how programming expertise can be used to increase the degree of autonomy in synthesis control, in particular by automating the selection of an appropriate data structure implementation.     

What experts want from programming languages

Abstract

This paper contrasts the aspirations of general-purpose programming language designers with some evidence about expert problem-solving and programming behaviour. The contrast is summarized in a rough wish-list of what experts want from general-purpose programming languages. The programmers' wish-list differs from the aspirations of language designers less in detail than in emphasis: whereas the designers emphasize well-foundedness and correctness, the expert programmers emphasize utility, control, and efficiency. It is argued that a programming language is a tool, not a panacea; tools make easy the tasks for which they are designed, but the outcome depends on the intention and expertise of the wielder.     

Knowledge-based facility planning: a review and a framework

The knowledge-based facility planning (KBFP) problem is reviewed. The aim of KBFP is to provide a more comprehensive planning package for users so that their expertise can be augmented with proven knowledge, and yield significantly better plans. The categories reviewed include facilities equipment selection, software model selection, and the generative task of creating a facility planning solution. The employed problem representation and problem-solving techniques are reviewed. Finally, the development of an integrated framework for KBFP is discussed.     

The structure and content of programming knowledge: Disentangling training and language effects in theories of skill development

This article reviews the extensive literature emerging from studies concerned with skill acquisition and the development of knowledge representation in programming. In particular, it focuses upon theories of program comprehension that suggest programming knowledge can be described in terms of stereotypical knowledge structures that can in some way capture programming expertise independently of the programming language used and in isolation from a programmer's specific training experience. An attempt is made to demonstrate why existing views are inappropriate. On the one hand, programs are represented in terms of a variety of formal notations ranging from the quasi‐mathematical to the near textual. It is argued that different languages may lead to different forms of knowledge representation, perhaps emphasizing certain structures at the expense of others or facilitating particular strategies. On the other hand, programmers are typically taught problem‐solving techniques that suggest a strict approach to problem decomposition. Hence, it seems likely that another factor that may mediate the development of knowledge representation, and that has not received significant attention elsewhere, is related to the training experience that programmers typically encounter. In this article, recent empirical studies that have addressed these issues are reviewed, and the implications of these studies for theories of skill acquisition and for knowledge representation are discussed. In conclusion, a more extensive account of knowledge representation in programming is presented that emphasizes training effects and the role played by specific language features in the development of knowledge representation within the programming domain.     

An approach to expert control of interactive software systems

Expert problem-solving strategies in many domains require the use of detailed mathematical techniques coupled with experiential knowledge about how and when to use the appropriate techniques. In many of these domains, such techniques are made available to experts in large software packages. In attempting to build expert systems for these domains, we wish to make use of these packages, and are therefore faced with an important problem: how to integrate the existing software, and knowledge about its use, into a practical expert system. The expert knowledge is used, in dynamic selection and interpretation of appropriate programs and parameters, to reach a successful goal in the problem solving. We describe the framework of a hybrid expert system for representing problem-solving knowledge in these domains. This hybrid system may be characterized as consisting of a production system and mathematical methods. The software package is reorganized as necessary to map it into the mathematical-method representation of a hybrid system. This approach has evolved out of an effort to build an expert system for performing well-log analysis, ELAS (expert log analysis system).     

An expert system on design of liquid-retaining structures with blackboard architecture

Abstract: The design of liquid‐retaining structures involves many decisions to be made by the designer based on rules of thumb, heuristics, judgement, codes of practice and previous experience. Structural design problems are often ill structured and there is a need to develop programming environments that can incorporate engineering judgement along with algorithmic tools. Recent developments in artificial intelligence have made it possible to develop an expert system that can provide expert advice to the user in the selection of design criteria and design parameters. This paper introduces the development of an expert system in the design of liquid‐retaining structures using blackboard architecture. An expert system shell, Visual Rule Studio, is employed to facilitate the development of this prototype system. It is a coupled system combining symbolic processing with traditional numerical processing. The expert system developed is based on British Standards Code of Practice BS8007. Explanations are made to assist inexperienced designers or civil engineering students to learn how to design liquid‐retaining structures effectively and sustainably in their design practices. The use of this expert system in disseminating heuristic knowledge and experience to practitioners and engineering students is discussed.     

The development of an expert system for online company database selection

Abstract: This paper describes a project undertaken in the Department of Information and Library Studies at Loughborough University to develop a prototype expert system to assist with the selection of online business databases for British company information. The project was funded by the British Library Research and Development Department for 21 months, commencing July 1990. Specific phases of the project comprised a literature survey, knowledge acquisition involving experts in online searching, the design and development of a system called CIDA (Company Information Database Adviser) which was some 4Mb in size, and a user evaluation of this prototype. The study demonstrated that expertise in business database selection can successfully be distilled into a number of rules which can be applied by an expert system.     

CORRECTING REAL-WORD SPELLING ERRORS USING A MODEL OF THE PROBLEM-SOLVING CONTEXT

This paper explores the application of a rich model of pragmatic context to the problem of identifying and correcting real-word spelling errors. Results suggest that such a model can be useful for generating and ranking a list of possible corrections according to their contextual relevance. In the domain of expert consultation discourse, a mode! of pragmatic context must represent not only the user's domain plans, but also the problem-solving processes that explore alternative plans, refining and instantiating the intended plan, and the connections between those problem-solving moves and their resulting discourse manifestations. In the model presented, metaplans are used to represent these problem-solving and discourse levels, while heuristics that take into account the user's problem-solving strategies and world knowledge serve to rank the relative likelihood of different possible next queries. An implementation of this model has been used to suggest pragmatically coherent interpretations that can be matched against a partial parse of the input in order to generate possible corrections for real-word spelling errors.     

Statistical expert systems

Abstract: Two types of expert system which involve statistical expertise are statistical consulting programs and programs which find patterns in databases. Consulting programs can now be built quickly using programming tools. Most expert systems include mechanisms for reasoning under uncertainty. Methods under investigation include fuzzy logic, Dempster-Shafer theory, Bayesian analysis and various ad hoc methods. Learning systems use statistics to infer inductive rules, and statistical reasoning can also be used to evaluate the performance of expert systems. The use of a prototype statistical expert system, XSAMPLE, is demonstrated, as a system to handle a consulting session with a statistically moderately advanced user.     

Research and Design of a Fuzzy Neural Expert System

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.     

A test of the usefulness of surveys in identifying potential expert systems applications in tax planning

Before expert systems can be developed to assist in tax practice, hundreds of tasks must be analyzed to determine which are most appropriate for system development. The purpose of this research are to gain an understanding of the tax planning applications domain and to test the validity of using a questionnaire in task selection. The most critical subset of the planning tasks was analyzed concerning characteristics that would indicate expertise. A questionnaire was used to obtain feedback from firm tax partners about the appropriateness of the subset of planning tasks for expert system development. The data gave a preliminary indication that corporate alternative minimum tax planning was the most viable task. A panel of tax expert systems specialists concurred. Further study of the questionnaire use was recommended.     

Visual programming environment for hybrid expert systems

This paper proposes an architecture for hybrid expert system development which combines expert problem-solving functions and other conventional computational functions by visual programming technology. The visual programming technique is used both for task-specific knowledge representation and for procedural programming for connecting functional components. In knowledge representation, knowledge is visually represented in the form of decision tables and decision trees. In procedural visual programming, each functional object is displayed as a box-shaped icon with accessible ports which are connected by wires on the graphic editor. Based on the architecture, an expert system shell for the classification task, called HOLON/VP(DT), is incorporated in a visual programming system called HOLON/VP. This paper briefly describes the tool and its evaluation based on some application systems developed with the tool.     

The development of a knowledge-based system for information systems project development consulting

The foundation for a successful software development project is a well-conceived project plan which establishes the overall framework for effective project management. Two major functions of planning an information systems development project are: 1) the selection of an appropriate development strategy; and 2) the assessment of risk associated with the development of the system. This paper describes the development of a knowledge-based expert system to assist in choosing the most appropriate development approach to use when planning an information systems development project and when assessing the risk associated with that project. The knowledge base consists of a set of rules addressing both tasks. Case examples of the use of the expert system are also given.     

Developmental aspects of expertise: Rationality and generalization

Successful attempts to explain expertise in human beings, or to capture its properties in expert systems, will have to contend with issues of rationality and generalization. Rationality and generalization pose enough difficulties on a purely synchronic basis. But an account of expertise must be diachronic—it must account for the development of rationality and generalization, even in those who are already experts. We describe the obstacles in the path of standard approaches to rationality and generalization, and present an alternative, interactivist treatment of rationality and its development (space forbids us to do likewise for generalization). In the interactivist account, rationality cannot be defined in general as adherence to the rules of a system of formal logic; we propose instead that rationality be understood in terms of the development of negative knowledge—knowing what kinds of errors to avoid. We examine the development of negative knowledge using examples from the history of science, and consider the consequences of an orientation towards negative knowledge for classroom instruction as well as the development of expert systems.     

R1-soar: an experiment in knowledge-intensive programming in a problem-solving architecture

This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.     

The role of expertise research and human factors in capturing, explaining, and producing superior performance

OBJECTIVES: The goal of this article is to identify some of the major trends and findings in expertise research and their connections to human factors. BACKGROUND: Progress in the study of superior human performance has come from improved methods of measuring expertise and the development of better tools for revealing the mechanisms that support expert performance, such as protocol analysis and eye tracking. METHODS: We review some of the challenges of capturing superior human performance in the laboratory and the means by which the expert performance approach may overcome such challenges. We then discuss applications of the expert performance approach to a handful of domains that have long been of interest to human factors researchers. RESULTS: Experts depend heavily on domain-specific knowledge for superior performance, and such knowledge enables the expert to anticipate and prepare for future actions more efficiently. Training programs designed to focus learners' attention on task-related knowledge and skills critical to expert performance have shown promise in facilitating skill acquisition among nonexperts and in reducing errors by experts on representative tasks. CONCLUSIONS: Although significant challenges remain, there is encouraging progress in domains such as sports, aviation, and medicine in understanding some of the mechanisms underlying human expertise and in structuring training and tools to improve skilled performance. APPLICATIONS: Knowledge engineering techniques can capture expert knowledge and preserve it for organizations and for the development of expert systems. Understanding the mechanisms that underlie expert performance may provide insights into the structuring of better training programs for improvingskill and in designing systems to support professional expertise.     

A unification-based approach for knowledge base verification

Abstract: Knowledge base verification, a part of the validation process in expert system development, includes checking the knowledge base for completeness and consistency to guard against a variety of errors that can arise during the process of transferring expertise from a human expert to a computer system. Regardless of how an expert system is developed, its developers can profit from a systematic check of the knowledge base without gathering extensive data for test runs, even before the full reasoning mechanism is functioning. Until recently knowledge base verification has been largely ignored, which has led to expert systems with knowledge base errors and no safety factors for correctness. We propose a unification-based approach for verification of a knowledge base represented in the form of production rules and facts. This approach can determine conflicting, redundant, subsumed and circular rules; redundant if-conditions in rules; dead-end rules; and cycles and contradiction in rules.