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Issue title: Special Issue on Agent-oriented Software Development Methodologies
Article type: Research Article
Authors: Xiao, Liang | Greer, Des
Affiliations: School of Computer Science, Queen's University Belfast, Belfast, BT7 1NN, UK. Tel.: +44 (0)28 9097 4656; Fax: +44 (0)28 9097 5666; E-mail: l.xiao@qub.ac.uk, des.greer@qub.ac.uk | Department of Computing, Oxford Brookes University, Oxford OX33 1HX, UK
Abstract: Multi-Agent Systems (MAS) have become increasingly mature, but this maturity does not make the traditional Object Oriented (OO) approaches obsolete. On the contrary, building MAS in combination with OO constructs allows the reuse of existing components. Similarly, OO methodologies can benefit from extension towards an agent abstraction and so make use of the methods and tools for MAS. The Agent-Rule-Class (ARC) framework is proposed as an approach that builds agents upon traditional OO system components and makes use of business rules to dictate agent behaviours, aided by the OO components. By modelling agent knowledge in business rules, the proposed paradigm provides a straightforward means to develop agent-oriented systems based on the existing object-oriented systems and offers features that are otherwise difficult to achieve in OO systems. A Structural Model and a Behavioural Model are the central components in the ARC framework for agent-oriented system modelling. A supporting tool has been developed to ensure that agents implement up-to-date requirements from business people, reflecting desired current behaviours, without the need for frequent system rebuilds. The ARC framework provides a complete MAS development process supported by a new process model more suited to collaboration between OO developers, Agent Oriented (AO) developers and domain experts. The main outcome of using ARC is better adaptivity. The ARC framework is illustrated with a rail track example.
Keywords: Adaptivity, agent-oriented software engineering, business rule, design model, object-oriented software engineering, requirements modelling, UML
DOI: 10.3233/MGS-2006-2403
Journal: Multiagent and Grid Systems, vol. 2, no. 4, pp. 325-351, 2006
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