Multiagent Systems Engineering: the Analysis Phase

Abstract : This report describes the Analysis phase of the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. The goal of MaSE is to guide a system developer from an initial system specification to a multiagent system implementation. This is done by directing the designer through this set of inter-related system models. Although the majority of the MaSE models are graphical, the underlying semantics clearly and unambiguously defines specific relationships between the graphical models. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE is designed to be applied iteratively. Under normal circumstances, we would expect a designer to move through each step multiple times, moving back and forth between models to ensure each model is complete and consistent. While this is common practice using most design methodologies, MaSE was specifically designed to support this process by formally capturing the relationships between the models. MaSE is independent of a particular multiagent system architecture, agent architecture, programming language, or communication framework. Systems designed using MaSE can be implemented in a variety ways. For example, a system could be designed and implemented that included a heterogeneous mix of agent architectures using any one of a number of existing underlying communication frameworks. The ultimate goal of MaSE and agent Tool is the automatic generation of code that is correct with respect to the original system specification.

[1]  Elizabeth A. Kendall,et al.  Capturing And Structuring Goals: Analysis Patterns , 1998, EuroPLoP.

[2]  Carlos Angel Iglesias Fernandez,et al.  A survey of agent-oriented methodologies , 1999 .

[3]  Scott A. DeLoach,et al.  Heterogeneous Database Integration Using Agent- Oriented Information Systems * , 2000 .

[4]  Gerhard Weiss,et al.  Multiagent Systems , 1999 .

[5]  Cristiano Castelfranchi,et al.  Proceedings of the 7th International Workshop on Intelligent Agents VII. Agent Theories Architectures and Languages , 2000 .

[6]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[7]  Anand S. Rao,et al.  A Methodology and Modelling Technique for Systems of BDI Agents , 1996, MAAMAW.

[8]  Timothy W. Finin,et al.  KQML as an agent communication language , 1994, CIKM '94.

[9]  Scott A. DeLoach,et al.  Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, 2001. Developing Multiagent Systems with agentTool , 2022 .

[10]  Hyacinth S. Nwana,et al.  ZEUS: a toolkit and approach for building distributed multi-agent systems , 1999, AGENTS '99.

[11]  Nicholas R. Jennings,et al.  Agent Theories, Architectures, and Languages: A Survey , 1995, ECAI Workshop on Agent Theories, Architectures, and Languages.

[12]  Michael M. Cox,et al.  A Problem Representation Approach for Decision Support Systems , 2000 .

[13]  K. Sycara,et al.  This Is a Publication of the American Association for Artificial Intelligence Multiagent Systems Multiagent System Issues and Challenges Individual Agent Reasoning Task Allocation Multiagent Planning Recognizing and Resolving Conflicts Managing Communication Modeling Other Agents Managing Resources , 2022 .