Conceptual information processing: A robust approach to KBS-DBMS integration

Integrating the respective functionality and architectural features of knowledge base and data base management systems is a topic of considerable interest. Several aspects of this topic and associated issues are addressed. The significance of integration and the problems associated with accomplishing that integration are discussed. The shortcomings of current approaches to integration and the need to fuse the capabilities of both knowledge base and data base management systems motivates the investigation of information processing paradigms. One such paradigm is concept based processing, i.e., processing based on concepts and conceptual relations. An approach to robust knowledge and data base system integration is discussed by addressing progress made in the development of an experimental model for conceptual information processing

Towards a Reliable Framework of Uncertainty-Based Group Decision Support System

This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.Comment: Accepted paper in IEEE-ICDM2010; Print ISBN: 978-1-4244-9244-

KNOWLEDGE SHARING AND NEGOTIATION SUPPORT IN MULTIPERSON DECISION SUPPORT SYSTEMS

A number of DSS for supporting decisions by more than one person have been proposed. These can be categorized by spatial distance (local vs. remote), temporal distance (meeting vs. mailing), commonality of goals (cooperation vs. bargaining), and control (democratic vs. hierarchical). Existing frameworks for model management in single-user DSS seem insufficient for such systems. This paper views multiperson DSS as a loosely coupled system of model and data bases which may be human (the DSS builders and users) or computerized. The systems components have different knowledge bases and may have different interests. Their interaction is characterized by knowledge sharing for uncertainty reduction and cooperative problem-solving, and negotiation for view integration, consensus-seeking, and compromise. Requirements for the different types of multiperson DSS can be formalized as application-level communications protocols. Based on a literature review and recent experience with a number of multiperson DSS prototypes, artificial intelligence-based message-passing protocols are compared with database-centered approaches and model-based techniques, such as multicriteria decision making.Information Systems Working Papers Serie

KNOWLEDGE SHARING AND NEGOTIATION SUPPORT IN MULTIPERSON DECISION SUPPORT SYSTEMS

A number of DSS for supporting decisions by more than one person have been proposed. These can be categorized by spatial distance (local vs. remote), temporal distance (meeting vs. mailing), commonality of goals (cooperation vs. bargaining), and control (democratic vs. hierarchical). Existing frameworks for model management in single-user DSS seem insufficient for such systems. This paper views multiperson DSS as a loosely coupled system of model and data bases which may be human (the DSS builders and users) or computerized. The systems components have different knowledge bases and may have different interests. Their interaction is characterized by knowledge sharing for uncertainty reduction and cooperative problem-solving, and negotiation for view integration, consensus-seeking, and compromise. Requirements for the different types of multiperson DSS can be formalized as application-level communications protocols. Based on a literature review and recent experience with a number of multiperson DSS prototypes, artificial intelligence-based message-passing protocols are compared with database-centered approaches and model-based techniques, such as multicriteria decision making.Information Systems Working Papers Serie

Composite load spectra for select space propulsion structural components

The objective of this program is to develop generic load models with multiple levels of progressive sophistication to simulate the composite load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen (LOX) posts and system ducting. These models will be developed using two independent approaches. The first approach consists of using state-of-the-art probabilistic methods to describe the individual loading conditions and combinations of these loading conditions to synthesize the composite load spectra simulation. The methodology required to combine the various individual load simulation models (hot-gas dynamic, vibrations, instantaneous position, centrifugal field, etc.) into composite load spectra simulation models will be developed under this program. A computer code incorporating the various individual and composite load spectra models will be developed to construct the specific load model desired. The second approach, which is covered under the options portion of the contract, will consist of developing coupled models for composite load spectra simulation which combine the (deterministic) models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients will then be determined using advanced probabilistic simulation methods with and without strategically selected experimental data. This report covers the efforts of the third year of the contract. The overall program status is that the turbine blade loads have been completed and implemented. The transfer duct loads are defined and are being implemented. The thermal loads for all components are defined and coding is being developed. A dynamic pressure load model is under development. The parallel work on the probabilistic methodology is essentially completed. The overall effort is being integrated in an expert system code specifically developed for this project