Michael Wooldridge

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).

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):

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.

Agent-Oriented Software Engineering

The ATAL workshops focus on the links between the theory and practice of intelligent agents. One aspect of this, which is steadily growing in importance, is the idea of agent technology as a software engineering paradigm. Previous ATAL workshops have had special tracks on programming languages for agent-oriented development, and methodologies for agent system development. ATAL-99 aims to build on this experience by focussing on the wider issues of agents as a software engineering paradigm.

Agent-based software engineering

The technology of intelligent agents and multi-agent systems is expected to alter radically the way in which complex, distributed, open systems are conceptualised and implemented. The paper considers the problem of building a multi-agent system as a software engineering enterprise. Three issues are focused on: how agents might be specified; how these specifications might be refined or otherwise transformed into efficient implementations: and how implemented agents and multi-agent systems might subsequently be verified, to show that they are correct with respect to their specifications. These issues are discussed with reference to a number of case studies. The paper concludes by setting out some issues and open problems for future research.

Applications of intelligent agents

Intelligent agents are a new paradigm for developing software applications. More than this, agent-based computing has been hailed as ‘the next significant breakthrough in software development’ (Sargent, 1992), and ‘the new revolution in software’ (Ovum, 1994). Currently, agents are the focus of intense interest on the part of many sub-fields of computer science and artificial intelligence. Agents are being used in an increasingly wide variety of applications, ranging from comparatively small systems such as email filters to large, open, complex, mission critical systems such as air traffic control. At first sight, it may appear that such extremely different types of system can have little in common. And yet this is not the case: in both, the key abstraction used is that of an agent Our aim in this article is to help the reader to understand why agent technology is seen as a fundamentally important new tool for building such a wide array of systems. More precisely, our aims are five-fold: to introduce the reader to the concept of an agent and agent-based systems, to help the reader to recognize the domain characteristics that indicate the appropriateness of an agent-based solution, to introduce the main application areas in which agent technology has been successfully deployed to date, to identify the main obstacles that lie in the way of the agent system developer, and finally to provide a guide to the remainder of this book.

The Belief-Desire-Intention Model of Agency

Within the ATAL community, the belief-desire-intention (BDI) model has come to be possibly the best known and best studied model of practical reasoning agents. There are several reasons for its success, but perhaps the most compelling are that the BDI model combines a respectable philosophical model of human practical reasoning, (originally developed by Michael Bratman [1]), a number of implementations (in the IRMA architecture [2] and the various PRS-like systems currently available [7]), several successful applications (including the now-famous fault diagnosis system for the space shuttle, as well as factory process control systems and business process management [8]), and finally, an elegant abstract logical semantics, which have been taken up and elaborated upon widely within the agent research community [14, 16].