688 research outputs found
Ada in AI or AI in Ada. On developing a rationale for integration
The use of Ada as an Artificial Intelligence (AI) language is gaining interest in the NASA Community, i.e., by parties who have a need to deploy Knowledge Based-Systems (KBS) compatible with the use of Ada as the software standard for the Space Station. A fair number of KBS and pseudo-KBS implementations in Ada exist today. Currently, no widely used guidelines exist to compare and evaluate these with one another. The lack of guidelines illustrates a fundamental problem inherent in trying to compare and evaluate implementations of any sort in languages that are procedural or imperative in style, such as Ada, with those in languages that are functional in style, such as Lisp. Discussed are the strengths and weakness of using Ada as an AI language and a preliminary analysis provided of factors needed for the development of criteria for the integration of these two families of languages and the environments in which they are implemented. The intent for developing such criteria is to have a logical rationale that may be used to guide the development of Ada tools and methodology to support KBS requirements, and to identify those AI technology components that may most readily and effectively be deployed in Ada
The nature and evaluation of commercial expert system building tools, revision 1
This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria
Tools for modelling support and construction of optimization applications
We argue the case for an open systems approach towards modelling and application support. We discuss how the 'usability' and 'skills' analysis naturally leads to a viable strategy for integrating application construction with modelling tools and optimizers. The role of the implementation environment is also seen to be critical in that it is retained as a building block within the resulting system
Expert-System Shells : Very-High-Level Languages for Artificial Intelligence
Expert-system shells are discussed as very-high-level programming languages for knowledge engineering. Based on a category/domain distinction for expert systems the concept of expert-system shells is explained using seven classifications. A proposal for a shell-development policy is sketched. The conclusions express concern about overemphasis on shell surfaces
Utilization of Expert Systems in the Work Place: Performing Project Software Cost Estimation on Training Systems
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
SHELL-BASED EXPERT SYSTEMS IN BUSINESS: A RETURN ON INVESTMENT PERSPECTIVE
This paper examines an important issue emerging in information systems management--the decision to proceed with an expert system application in a business setting. The focus is knowledge based systems at the lower end of the complexity spectrum--small, very focused systems that can be implemented by the use of shell-based development environments. This group represents the majority of expert systems that are currently being implemented and has some characteristics quite different from the larger systems. A classification scheme is suggested to differentiate three levels of ES development, from multi-million dollar life cycle cost ES environments to those that are in the low five figure range. The Low End segment of the range, the focus of this paper, is characterized by lower unit costs, powerful development tools and a large number of small, successful applications. The important role of Low End systems is discussed, with particular emphasis on their relatively high yield in standalone applications. Such systems do not meet the AI demands of moderately or very complex problems but there is a surprising breadth in their use. A group of key success factors for Low End systems is proposed, based on a synthesis of the applications literature. To operationalize these factors, three actual cases using Low End technology--from marketing, government and agribusiness-- are briefly described. Low End systems are not all gain. Their low unit costs can often mask the risks of proceeding headlong into an application without careful examination of the variables that can predict successful results. An agenda for action is offered for specific management policies for the planning of knowledge-based applications
Introduction to artificial intelligence and expert systems : a special report developed for CPAs seeking to become familiar with artificial intelligence and expert systems technology; Management advisory services special report
https://egrove.olemiss.edu/aicpa_guides/1155/thumbnail.jp
COLAB : a hybrid knowledge representation and compilation laboratory
Knowledge bases for real-world domains such as mechanical engineering require expressive and efficient representation and processing tools. We pursue a declarative-compilative approach to knowledge engineering. While Horn logic (as implemented in PROLOG) is well-suited for representing relational clauses, other kinds of declarative knowledge call for hybrid extensions: functional dependencies and higher-order knowledge should be modeled directly. Forward (bottom-up) reasoning should be integrated with backward (top-down) reasoning. Constraint propagation should be used wherever possible instead of search-intensive resolution. Taxonomic knowledge should be classified into an intuitive subsumption hierarchy. Our LISP-based tools provide direct translators of these declarative representations into abstract machines such as an extended Warren Abstract Machine (WAM) and specialized inference engines that are interfaced to each other. More importantly, we provide source-to-source transformers between various knowledge types, both for user convenience and machine efficiency. These formalisms with their translators and transformers have been developed as part of COLAB, a compilation laboratory for studying what we call, respectively, "vertical\u27; and "horizontal\u27; compilation of knowledge, as well as for exploring the synergetic collaboration of the knowledge representation formalisms. A case study in the realm of mechanical engineering has been an important driving force behind the development of COLAB. It will be used as the source of examples throughout the paper when discussing the enhanced formalisms, the hybrid representation architecture, and the compilers
Knowledge-Based Systems. Overview and Selected Examples
The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology to create expert systems that have practical applications.
By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, models of complex processes are made understandable and available to non-technical users.
Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way, e.g., "Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China."
This paper gives an overview of some of the expert systems that have been considered, compared or assessed during the course of our research, and a brief introduction to some of our related in-house research topics
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