The integration of an intelligent knowledge-based system into engineering software using the blackboard structure

Over recent years there has been an increase in the use of generic Computational Fluid Dynamics (CFD) software packages spread across various application fields. This has created the need for the integration of expertise into CFD software. Expertise can be integrated into CFD software in the form of an Intelligent Knowledge-Based System (IKBS). The advantages of integrating intelligence into generic engineering software are discussed with a special view to software engineering considerations. The software modelling cycle of a typical engineering problem is identified and the respective expertise and user control needed for each modelling phase is shown. The requirements of an IKBS for CFD software are discussed and compared to current practice. The blackboard software architecture is presented. This is shown to be appropriate for the integration of an IKBS into an engineering software package. This is demonstrated through the presentation of the prototype CFD software package FLOWES.

A blackboard architecture for a hybrid CBR system for scientific software

This paper describes the use of a blackboard architecture for building a hybrid case based reasoning (CBR) system. The Smartfire fire field modelling package has been built using this architecture and includes a CBR component. It allows the integration into the system of qualitative spatial reasoning knowledge from domain experts. The system can be used for the automatic set-up of fire field models. This enables fire safety practitioners who are not expert in modelling techniques to use a fire modelling tool. The paper discusses the integrating powers of the architecture, which is based on a common knowledge representation comprising a metric diagram and place vocabulary and mechanisms for adaptation and conflict resolution built on the Blackboard.

Fire field modelling using the SMARTFIRE automated dynamic solution control environment

SMARTFIRE is a fire field model based on an open architecture integrated CFD code and knowledge-based system. It makes use of the expert system to assist the user in setting up the problem specification and new computational techniques such as Group Solvers to reduce the computational effort involved in solving the equations. This paper concentrates on recent research into the use of artificial intelligence techniques to assist in dynamic solution control of fire scenarios being simulated using fire field modelling techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxations using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate enhanced solution reliability due to obtaining acceptable convergence within each time step unlike some of the comparison simulations.

An architecture of multiple heterogeneous case-based reasoning employing agent technologies

This paper presents an investigation into applying Case-Based Reasoning to Multiple Heterogeneous Case Bases using agents. The adaptive CBR process and the architecture of the system are presented. A case study is presented to illustrate and evaluate the approach. The process of creating and maintaining the dynamic data structures is discussed. The similarity metrics employed by the system are used to support the process of optimisation of the collaboration between the agents which is based on the use of a blackboard architecture. The blackboard architecture is shown to support the efficient collaboration between the agents to achieve an efficient overall CBR solution, while using case-based reasoning methods to allow the overall system to adapt and “learn” new collaborative strategies for achieving the aims of the overall CBR problem solving process.

Learning styles, personalisation and adaptable e-learning

Common Learning Management Systems (for example Moodle [1] and Blackboard [2]) are limited in the amount of personalisation that they can offer the learner. They are used widely and do offer a number of tools for instructors to enable them to create and manage courses, however, they do not allow for the learner to have a unique personalised learning experience. The e-Learning platform iLearn offers personalisation for the learner in a number of ways and one way is to offer the specific learning material to the learner based on the learner's learning style. Learning styles and how we learn is a vast research area. Brusilovsky and Millan [3] state that learning styles are typically defined as the way people prefer to learn. Examples of commonly used learning styles are Kolb Learning Styles Theory [4], Felder and Silverman Index of Learning Styles [5], VARK [6] and Honey and Mumford Index of Learning Styles [7] and many research projects (SMILE [8], INSPIRE [9], iWeaver [10] amonst others) attempt to incorporate these learning styles into adaptive e-Learning systems. This paper describes how learning styles are currently being used within the area of adaptive e-Learning. The paper then gives an overview of the iLearn project and also how iLearn is using the VARK learning style to enhance the platform's personalisation and adaptability for the learner. This research also describes the system's design and how the learning style is incorporated into the system design and semantic framework within the learner's profile.

Enhancing the numerical performance of fire field models (CMS Paper)

This paper describes two new techniques designed to enhance the performance of fire field modelling software. The two techniques are "group solvers" and automated dynamic control of the solution process, both of which are currently under development within the SMARTFIRE Computational Fluid Dynamics environment. The "group solver" is a derivation of common solver techniques used to obtain numerical solutions to the algebraic equations associated with fire field modelling. The purpose of "group solvers" is to reduce the computational overheads associated with traditional numerical solvers typically used in fire field modelling applications. In an example, discussed in this paper, the group solver is shown to provide a 37% saving in computational time compared with a traditional solver. The second technique is the automated dynamic control of the solution process, which is achieved through the use of artificial intelligence techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxation using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate the potential for enhanced solution reliability due to obtaining acceptable convergence within each time step, unlike some of the comparison simulations.