Nicholas R. Jennings

Intelligent agents: theory and practice

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

A Roadmap of Agent Research and Development

This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.

The Gaia Methodology for Agent-Oriented Analysis and Design

This article presents Gaia: a methodology for agent-oriented analysis and design. The Gaia methodology is both general, in that it is applicable to a wide range of multi-agent systems, and comprehensive, in that it deals with both the macro-level (societal) and the micro-level (agent) aspects of systems. Gaia is founded on the view of a multi-agent system as a computational organisation consisting of various interacting roles. We illustrate Gaia through a case study (an agent-based business process management system).

On agent-based software engineering

Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.

Agent theories, architectures, and languages: a survey

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. In this article, we present a survey of what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. The article also includes a short review of current and potential applications of agent technology, and closes with a glossary of key terms, an annotated list of systems, and a detailed bibliography. Pointers to further reading are provided throughout.

Automated Negotiation: Prospects, Methods and Challenges

An increasing number of computer systems are being viewed in terms of autonomous agents. There are two main drivers to this trend. Firstly, agents are being advocated as a next generation model for engineering complex, distributed systems (Jennings 2000; Wooldridge 1997). Secondly, agents are being used as an overarching framework for bringing together the component AI subdisciplines that are necessary to design and build intelligent entities (Nilsson 1998; Russel and Norvig 1995). While there is still much debate about the precise nature of agenthood, an increasing number of researchers find the following characterisation useful (Wooldridge 1997):

An agent-based approach for building complex software systems

uilding high-quality, industrialstrength software is difficult. Indeed, it has been argued that developing such software in domains like telecommunications, industrial control, and business process management represents one of the most complex construction tasks humans undertake. Against this background, a wide range of software engineering paradigms have been devised. Each successive development either claims to make the engineering process easier or promises to extend the complexity of applications that can feasibly be built. Although evidence is emerging to support these claims, researchers continue to strive for more effective techniques. To this end, this article will argue that analyzing, designing, and implementing complex software systems as a collection of interacting, autonomous agents (that is, as a multiagent system [4]) affords software engineers a number of significant advantages over contemporary methods. This is not to say that agent-oriented software engineering represents a silver bullet [2]—there is no evidence to suggest it will represent an order of magnitude improvement in productivity. However, the increasing number of deployed applications [4, 8] bears testament to the potential advantages that accrue from such an approach. In seeking to demonstrate the efficacy of agent-oriented techniques, the most compelling argument would be to quantitatively show how their adoption improved the development process in a range of projects. However, such data is simply not available (as it is not for approaches like patterns, application frameworks, and componentware). Given this situation, the best that can be achieved is a qualitative justification of why agent-oriented approaches are well suited to engineering complex, distributed software systems.

Developing multiagent systems: The Gaia methodology

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 Gaias concepts and to show its use and effectiveness in different types of multiagent system.

Negotiation Decision Functions for Autonomous Agents

We present a formal model of negotiation between autonomous agents. The purpose of the negotiation is to reach an agreement about the provision of a service by one agent for another. The model defines a range of strategies and tactics that agents can employ to generate initial offers, evaluate proposals and offer counter proposals. The model is based on computationally tractable assumptions, demonstrated in the domain of business process management and empirically evaluated.

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.