XML-Based Data Model and Architecture for a Knowledge-Based Grid-Enabled Problem-Solving Environment for High-Throughput Biological Imaging

2008 ◽  
Vol 12 (2) ◽  
pp. 226-240 ◽  
Author(s):  
W.M. Ahmed ◽  
D. Lenz ◽  
Jia Liu ◽  
J.P. Robinson ◽  
A. Ghafoor
Keyword(s):  
Problem Solving ◽  
High Throughput ◽  
Data Model ◽  
Biological Imaging ◽  
Knowledge Based ◽  
Problem Solving Environment

AI-DEQSOL. A knowledge-based environment for numerical simulation of engineering problems described by partial differential equations

1992 ◽  
Vol 6 (3) ◽  
pp. 199-212 ◽  
Author(s):  
D. P. Finn ◽  
N. J. Hurley ◽  
N. Sagawa
Keyword(s):  
Numerical Simulation ◽  
Partial Differential Equations ◽  
Problem Solving ◽  
Differential Equations ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Problem Solving Environment ◽  
Engineering Problems ◽  
User Groups ◽  
Modelling Environment

This paper presents a knowledge-based problem solving environment for numerical simulation of problems described by partial differential equations (PDEs). The system aims to facilitate the simulation requirements of different user groups that include engineers, mathematicians and numerical analysts. To attain this objective, a flexible multi-perspective modelling environment is proposed which incorporates three natural modelling platforms, namely; a physical model, a mathematical model and a numerical model. The modelling environment is integrated with a sophisticated numerical solver. We believe that combination of an open modelling system with a basic numerical simulator provides a powerful problem solving environment capable of meeting the needs of these different user groups. The overall system architecture is based on automatic transformation using mathematical and numerical knowledge bases between the three identified models. The knowledge bases are organized in a frame based manner to reflect the hierarchical nature of the knowledge in PDEs and numerical algorithms. The object oriented paradigm is used to bind local rule bases to each frame and for implementing a global inference mechanism which works over the hierarchical knowledge structures. Evaluation of the modelling environment has indicated that engineers can tackle PDE based engineering problems without the necessity for detailed knowledge of mathematics or numerical techniques and mathematicians can examine the mathematical properties of PDEs without the requirement of numerical expertise.

scholarly journals Computational steering in the problem solving environment WBCSim

2011 ◽  
Vol 28 (7) ◽  
pp. 888-911
Author(s):  
Jiang Shu ◽  
Layne T. Watson ◽  
Naren Ramakrishnan ◽  
Frederick A. Kamke ◽  
Shubhangi Deshpande
Keyword(s):  
Problem Solving ◽  
Computational Steering ◽  
Problem Solving Environment

scholarly journals An experiment management component for the WBCSim problem solving environment

2004 ◽  
Vol 35 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Jiang Shu ◽  
Layne T Watson ◽  
Naren Ramakrishnan ◽  
Frederick A Kamke ◽  
Balazs G Zombori
Keyword(s):  
Problem Solving ◽  
Problem Solving Environment ◽  
Experiment Management

Configuration Tree Solver: A Technology for Automated Design and Configuration

1990 ◽  
Author(s):  
Alexander Kott ◽  
Gerald Agin ◽  
Dave Fawcett
Keyword(s):  
Problem Solving ◽  
General Model ◽  
General Class ◽  
Domain Knowledge ◽  
Automated Design ◽  
Automotive Component ◽  
Business Operation ◽  
Knowledge Based ◽  
Specific Configuration

Abstract Configuration is a process of generating a definitive description of a product or an order that satisfies a set of specified requirements and known constraints. Knowledge-based technology is an enabling factor in automation of configuration tasks found in the business operation. In this paper, we describe a configuration technique that is well suited for configuring “decomposable” artifacts with reasonably well defined structure and constraints. This technique may be classified as a member of a general class of decompositional approaches to configuration. The domain knowledge is structured as a general model of the artifact, an and-or hierarchy of the artifact’s elements, features, and characteristics. The model includes constraints and local specialists which are attached to the elements of the and-or-tree. Given the specific configuration requirements, the problem solving engine searches for a solution, a subtree, that satisfies the requirements and the applicable constraints. We describe an application of this approach that performs configuration and design of an automotive component.

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