Specifying Distributed Multi-Agent Systems in Chemical Reaction Metaphor *

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Specifying Distributed Multi-Agent Systems in Chemical Reaction Metaphor

This paper presents an application of Chemical Reaction Metaphor (CRM) in distributed multi-agent systems (MAS). The suitability of using CRM to model multi-agent systems is justified by CRM's capacity in specifying dynamic features of multi-agent systems. A case study in an agent-based e-learning system (course material updating) demonstrates how the CRM based language, Gamma, can be used to specify the architectures of multi-agent systems. The effectiveness of specifying multi-agent systems in CRM from the view point of software engineering is further justified by introducing a transformational method for implementing the specified multi-agent systems. A computation model with a tree-structured architecture is proposed to base the design of the specified multi-agent system during the implementation phase. A module language based on the computation model is introduced as an intermediate language to facilitate the translation of the specification of multi-agent systems. The multicast networking technology pragmatizes the implementation of communications and synchronization among distributed agents. The paper also discusses the feasibility of implementing an automatic translation from the Gamma specification to a program in the module language.Cet article pr\ue9sente une application de la m\ue9taphore de la r\ue9action chimique dans les syst\ue8mes multi agent distribu\ue9s. L'ad\ue9quation de la m\ue9taphore de la r\ue9action chimique pour mod\ue9liser des syst\ue8mes multi agent est justifi\ue9e par sa capacit\ue9 \ue0 sp\ue9cifier des caract\ue9ristiques dynamiques de tels syst\ue8mes. Une \ue9tude de cas d'un syst\ue8me d'apprentissage \ue9lectronique bas\ue9 sur des agents (actualisation du mat\ue9riel de cours) d\ue9montre comment Gamma, un langage bas\ue9 sur la m\ue9taphore de la r\ue9action chimique, peut \ueatre utilis\ue9 pour sp\ue9cifier l'architecture de syst\ue8mes multi agent. L'efficacit\ue9 de la sp\ue9cification de syst\ue8mes multi agent avec la m\ue9taphore de la r\ue9action chimique, du point de vue du g\ue9nie logiciel, est en outre justifi\ue9e par l'introduction d'une m\ue9thode de transformation permettant de mettre en \u153uvre les syst\ue8mes multi agent sp\ue9cifi\ue9s. Nous proposons un mod\ue8le de traitement avec une architecture structur\ue9e en arborescence afin de fonder la conception du syst\ue8me multi agent sp\ue9cifi\ue9 lors de la phase de mise en \u153uvre. Nous introduisons un langage de module bas\ue9 sur le mod\ue8le de traitement, \ue0 titre de langage interm\ue9diaire, pour faciliter la traduction de la sp\ue9cification des syst\ue8mes multi agent. La technologie des r\ue9seaux multidiffusion constitue le mod\ue8le de mise en \u153uvre des m\ue9canismes de communications et de synchronisation entre les agents distribu\ue9s. Cet article examine \ue9galement la faisabilit\ue9 de la mise en \u153uvre d'une traduction automatique, depuis la sp\ue9cification dans le langage Gamma, vers un programme dans le langage de module.NRC publication: Ye

A quality assessment framework for knowledge management software

CONTEXT: Knowledge is a strategic asset to any organisation due to its usefulness in supportinginnovation, performance improvement and competitive advantage. In order to gain the maximum benefit from knowledge, the effective management of various forms of knowledge is increasingly viewed as vital. A Knowledge Management System (KMS) is a class of Information System (IS) that manages organisational knowledge, and KMS software (KMSS) is a KMS component that can be used as a platform for managing various forms of knowledge. The evaluation of the effectiveness or quality of KMS software is challenging, and no systematic evidence exists on the quality evaluation of knowledge management software which considers the various aspects of Knowledge Management (KM) to ensure the effectiveness of a KMS.AIM: The overall aim is to formalise a quality assessment framework for knowledge management software (KMSS).METHOD: In order to achieve the aim, the research was planned and carried out in the stages identified in the software engineering research methods literature. The need for this research was identified through a mapping study of prior KMS research. The data collected through a Systematic Literature Review (SLR) and the evaluation of a KMSS prototype using a sample of 58 regular usersof knowledge management software were used as the main sources of data for the formalisation of the quality assessment framework. A test bed for empirical data collection was designed and implemented based on key principles of learning. A formalised quality assessment framework was applied to select knowledge management software and was evaluated for effectiveness. RESULTS: The final outcome of this research is a quality assessment framework consisting of 41 quality attributes categorised under content quality, platform quality and user satisfaction. A Quality Index was formulated by integrating these three categories of quality attributes to evaluate the quality of knowledge management software.CONCLUSION: This research generates novel contributions by presenting a framework for the quality assessment of knowledge management software, never previously available in the research. This framework is a valuable resource for any organisation or individual in selecting the most suitable knowledge management software by considering the quality attributes of the software