Ontologies offer significant benefits to multi-agent systems: interoperability, reusability, support for multi-agent system (MAS) development activities (such as system analysis and agent knowledge modeling) and support for MAS operation (such as agent communication and reasoning). This paper presents an ontology-based methodology, MOBMAS, for the analysis and design of multi-agent systems. MOBMAS is the first methodology that explicitly identifies and implements the various ways in which ontologies can be used in the MAS development process and integrated into the MAS model definitions. In this paper, we present comprehensive documentation and validation of MOBMAS.
This chapter provides a comparison of the 10 agent-oriented software engineering methodologies presented in the preceding chapters. An evaluation framework comprising process-related, technique-related, model-related and supportive-feature criteria is used in this comparison. As each application entails a different set of requirements that indicate which evaluation criteria are the most important and should be supported by the chosen methodology, the “best” methodology is dependent on the target application. The results provide a useful framework to assist the developer in selecting the most appropriate methodology for any target application.
Abstract.While there are a considerable number of software engineering methodologies for developing multi-agent systems, not much work has been reported on the evaluation and comparison of these methodologies. This paper presents a comparative analysis of five well-known MAS-development methodologies. The comparison is based on a feature analysis framework published previously [1]. This framework allows the comparative analysis to be made on a variety of evaluation criteria, covering both agent-oriented aspects and system engineering dimensions. The analysis also compares the methodologies in terms of their support for the steps in the development process, and for agent-oriented concept modeling.
Abstract. This paper proposes a comprehensive and multi-dimensional feature analysis framework for evaluating and comparing methodologies for developing multi-agent systems (MAS). Developed from a synthesis of various existing evaluation frameworks, the novelty of our framework lies in the high degree of its completeness and the relevance of its evaluation criteria. The paper also presents a pioneering effort in identifying the standard steps and concepts to be supported by a MAS-development process and models.
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