An Introduction to MultiAgent Systems.

Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

/pdf/developing-multiagent-systems-the-gaia-methodology-3qm0mwov2h.pdf

Developing multiagent systems: The Gaia methodology

Build your own intelligent agent system. Intelligent agent technology is a tool of modern computer science that can be used to engineer complex computer programmes that behave rationally in dynamic and changing environments. Applications range from small programmes that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This powerful technology is not widely used, however, as developing intelligent agent software requires high levels of training and skill. The authors of this book have developed and tested a methodology and tools for developing intelligent agent systems. With this methodology (Prometheus) developers can start agent-oriented designs and implementations easily from scratch saving valuable time and resources. Developing Intelligent Agent Systems not only answers the questions 'what are agents?' and 'why are they useful?' but also the crucial question: 'how do I design and build intelligent agent systems?' The book covers everything a practitioner needs to know to begin to effectively use this technology - including an introduction to the notion of agents, a description of the concepts involved, and a software engineering methodology.

Developing Intelligent Agent Systems: A Practical Guide

Agent-based computing is a promising approach for developing applications in complex domains. However, despite the great deal of research in the area, a number of challenges still need to be faced (i) to make agent-based computing a widely accepted paradigm in software engineering practice, and (ii) to turn agent-oriented software abstractions into practical tools for facing the complexity of modern application areas. In this paper, after a short introduction to the key concepts of agent-based computing (as they pertain to software engineering), we characterise the emerging key issues in multiagent systems (MASs) engineering. In particular, we show that such issues can be analysed in terms of three different “scales of observation”, i.e., in analogy with the scales of observation of physical phenomena, in terms of micro, macro, and meso scales. Based on this characterisation, we discuss, for each scale of observation, what are the peculiar engineering issues arising, the key research challenges to be solved, and the most promising research directions to be explored in the future.

/pdf/challenges-and-research-directions-in-agent-oriented-bajl998fan.pdf

Challenges and Research Directions in Agent-Oriented Software Engineering

Software agents offer a range of benefits to the development of complex software systems. However, before these benefits can be realised by the computing industry there is a need for an agent platform that can be accepted by industry. In this paper we describe the JACK agent platform: a mature and robust commercial product. We argue that JACK meets requirements such as familiarity, scalability and integratibility which make it suitable for adoption by industry. We also describe interesting features of JACK such as the use of capabilities for structuring agents, and JACK’s approach to teamwork which allows hierarchical team structures.

JACK intelligent agents: an industrial strength platform

This paper is concerned with improving the software engineering of agent-based open systems. It critiques the existing Gaia methodology in the light of a motivating example of intelligent home networks. It describes the ROADMAP1 methodology, which extends Gaia with four improvements - formal models of knowledge and the environment, role hierarchies, explicit representation of social structures and relationships, and incorporation of dynamic changes.

ROADMAP: extending the gaia methodology for complex open systems

Gaia (Wooldridge et al., 2000b) was the first complete methodology proposed for the analysis and design of MAS. However, the original version of Gaia suffered from the limitations of being suitable for the analysis and design of closed MAS and of adopting non-standard notation techniques. Several extensions to the basic Gaia methodology have been recently proposed to overcome these limitations. In this chapter, we summarize the key characteristics of the original Gaia methodology and present three extensions that have been proposed to improve Gaia and make it more suitable for the development of open MAS in complex environments.

The Gaia Methodology

In this paper, we introduce the ROADMAP meta-model, designed to describe intelligent adaptive systems in open environments, using agent concepts such as roles. Developing intelligent adaptive systems creates new challenges in engineering software quality attributes such as correctness and reliability. The ROADMAP meta-model captures our understanding of properties of intelligent adaptive systems and our perspective on organizational concepts such as roles. The meta-model does not solve specific engineering problems, but provides a clean high-level structure where engineering issues can be grouped and classified. Infrastructure to support these issues can then be put in place progressively with consistency. An informal evaluation of the meta-model and comparison to related work is also presented. We expect developers of AOSE methodologies, tools, programming languages and frameworks to benefit from understanding the design and structure of the ROADMAP meta-model. By adopting the meta-model, the resulting methodologies, tools and languages may inherit its desirable characteristics and better support the development of intelligent adaptive systems in open environments.

https://researchbank.swinburne.edu.au/file/380aeac8-ad09-4e7c-bd49-a4a107f98b2e/1/PDF (Accepted manuscript).pdf

The ROADMAP Meta-model for Intelligent Adaptive Multi-agent Systems in Open Environments

Gaia (Wooldridge et al.‚ 2000b) was the first complete methodology proposed for the analysis and design of MAS. However‚ the original version of Gaia suffered from the limitations of being suitable for the analysis and design of closed MAS and of adopting non-standard notation techniques. Several extensions to the basic Gaia methodology have been recently proposed to overcome these limitations. In this chapter‚ we summarize the key characteristics of the original Gaia methodology and present three extensions that have been proposed to improve Gaia and make it more suitable for the development of open MAS in complex environments.

The Gaia methodology: basic concepts and extensions

In this paper we describe our effort to merge two existing AOSE methodologies, Prometheus and ROADMAP, by isolating a set of general-purpose common elements. The remaining parts of the two methodologies are componentized into special purpose "value-adding" features. This approach empowers the developer to assemble a methodology tailored to the given project (and the application domain) by adding appropriate features to the common elements. The assembled methodology can be modified during development to support changing aspects of the system.

https://researchbank.swinburne.edu.au/file/681bb606-da5b-4de2-85f7-bd2eebd8101a/1/PDF (Accepted manuscript).pdf

Thomas Juan

Papers

ROADMAP: extending the gaia methodology for complex open systems

The Gaia Methodology

The ROADMAP Meta-model for Intelligent Adaptive Multi-agent Systems in Open Environments

The Gaia methodology: basic concepts and extensions

Assembling agent oriented software engineering methodologies from features