Representation and matching of knowledge to design digital systems

A knowledge-based expert system is described that provides an approach to solve a problem requiring an expert with considerable domain expertise and facts about available digital hardware building blocks. To design digital hardware systems from their high level VHDL (Very High Speed Integrated Circuit Hardware Description Language) representation to their finished form, a special data representation is required. This data representation as well as the functioning of the overall system is described

A practical approach to object based requirements analysis

Presented here is an approach developed at the Unisys Houston Operation Division, which supports the early identification of objects. This domain oriented analysis and development concept is based on entity relationship modeling and object data flow diagrams. These modeling techniques, based on the GOOD methodology developed at the Goddard Space Flight Center, support the translation of requirements into objects which represent the real-world problem domain. The goal is to establish a solid foundation of understanding before design begins, thereby giving greater assurance that the system will do what is desired by the customer. The transition from requirements to object oriented design is also promoted by having requirements described in terms of objects. Presented is a five step process by which objects are identified from the requirements to create a problem definition model. This process involves establishing a base line requirements list from which an object data flow diagram can be created. Entity-relationship modeling is used to facilitate the identification of objects from the requirements. An example is given of how semantic modeling may be used to improve the entity-relationship model and a brief discussion on how this approach might be used in a large scale development effort

Dynamic learning and refinement of preferences through keywords

The main goal in this work is to learn user preferences in situations where the objects to be treated are formed only by textual information and we continuously have information of selections made by the user. This work has been divided in two major parts: the first one including the algorithms and techniques to rank a set of alternatives, and the second one including the techniques to maintain the profile up to date. Regarding the first part, the goal is to evaluate an object of type text, i.e. given the user preferences to assign the degree of potential interest on that object. This will allow us to evaluate the set of alternatives and to sort them according to the user preferences. Concerning the second part, the main goal is to design a method to update the user profile, given the user selection from a set of alternatives in the first part. This method will allow to adapt a user profile in an unsupervised and dynamic way. To achieve these objectives it is necessary to fulfil the tasks discussed in this document and named below in the document organization

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Proceedings of the Thirteenth Annual Software Engineering Workshop

Topics covered in the workshop included studies and experiments conducted in the Software Engineering Laboratory (SEL), a cooperative effort of NASA Goddard Space Flight Center, the University of Maryland, and Computer Sciences Corporation; software models; software products; and software tools

A Functional Approach to Model Management.

A model management system (MMS) is a computer-based system which facilitates the management of a wide range of decision support models (DSMs). It is a vital component of a decision support system which helps decision makers using data and DSMs in an integrated fashion. It is desirable that an MMS is knowledge-based, flexible, independent and reflecting users\u27 view of a DSM. In addition, an MMS must manage DSMs at both the macro and micro levels. At the macro level, DSMs are described using descriptive attributes to supply users with general problem-solving capabilities. At the micro level, the components and mathematical structure of a DSM are expressed to facilitate DSM formulation. There are three two-level MMS approaches proposed in the literature. All three approaches suggest describing the details of a DSM using the frame system. However, there are problems using the frame system at the micro level. First, the frame system does not provide facilities for handling data of large volume. Second, there is neither design methodology nor evaluation criteria regarding the design of the frame system. Third, pre-defined frame constructs may not be able to encompass all DSMs used in a dynamic environment. Finally, designers of the frame system usually lack of the professional skills to design an appropriate structure. The functional MMS is proposed to overcome the deficiency. It is intended to provide the two-level model management capability with all the desirable features. The macro-level functional MMS is based on first-order logic which is the best-developed knowledge representation methodology so far. At the micro level, the foundation is on relational theory which has proven its usefulness in data management. Additionally, the definitional system used to describe the details of a DSM provides a natural way of developing a DSM in a hierarchical manner