1,721,280 research outputs found
Understanding Agent Systems
No full textAround ten years ago, when we were both PhD students, working on different but related aspects of artificial intelligence, we shared an office in the furthest corner of the Department of Computer Science at University College London. Our friendship began then, but our professional collaboration only really got going when we both left, one of us moving the few yards to the University of Westminster and the other further afield to the University of Warwick and later the University of Southampton. Nevertheless, we can trace back many of our inspirations to those days at UCL, in discussions with Derek Long, John Campbell, Maria Fox and John Wolstencroft, who all contributed to our initial enthusiasm for working in this area. On leaving UCL, however, we tried to bring our research interests together in the newly emerging area of agent-based systems, but found difficulties in communica tion with each other over basic terms and concepts, simply due to the immaturity of the field. In other words, the problems we had in finding a base on which to develop our ideas set us on a long path, over a number of years, resulting in our construction and refinement of a conceptual framework within which to define, analyse and ex plore different aspects of agents and multi-agents systems. This is the work reported in this book
On identifying and managing relationships in multi-agent systems
Full text linkMulti-agent systems result from interactions between individual agents. Through these interactions different kinds of relationships are formed, which can impact substantially on the overall system performance. However, the behaviour of agents cannot always be anticipated, especially when dealing with open and complex systems. Open agent systems must incorporate relationship management mechanisms to constrain agent actions and allow only desirable interactions. In consequence, in this paper we tackle two important issues. Firstly, in addressing management, we identify the range of different control mechanisms that are required and when they should be applied. Secondly, in addressing relationships, we present a model for identifying and characterising relationships in a manner that is application-neutral and amenable to automation
A Manifesto for Agent Technology: Towards Next Generation Computing
No full textThe European Commission's eEurope initiative aims to bring every citizen, home, school, business and administration online to create a digitally literate Europe. The value lies not in the objective itself, but in its ability to facilitate the advance of Europe into new ways of living and working. Just as in the first literacy revolution, our lives will change in ways never imagined. The vision of eEurope is underpinned by a technological infrastructure that is now taken for granted. Yet it provides us with the ability to pioneer radical new ways of doing business, of undertaking science, and, of managing our everyday activities. Key to this step change is the development of appropriate mechanisms to automate and improve existing tasks, to anticipate desired actions on our behalf (as human users) and to undertake them, while at the same time enabling us to stay involved and retain as much control as required. For many, these mechanisms are now being realised by agent technologies, which are already providing dramatic and sustained benefits in several business and industry domains, including B2B exchanges, supply chain management, car manufacturing, and so on. While there are many real successes of agent technologies to report, there is still much to be done in research and development for the full benefits to be achieved. This is especially true in the context of environments of pervasive computing devices that are envisaged in coming years. This paper describes the current state-of-the-art of agent technologies and identifies trends and challenges that will need to be addressed over the next 10 years to progress the field and realise the benefits. It offers a roadmap that is the result of discussions among participants from over 150 organisations including universities, research institutions, large multinational corporations and smaller IT start-up companies. The roadmap identifies successes and challenges, and points to future possibilities and demands; agent technologies are fundamental to the realisation of next generation computing
Identifying opportunities and constraints for goal achievement through relationship analysis
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Agent Hell: A Scenario of Worst Practices
No full textA little confusion goes a long way—too far—with software-based agents. Engineering discipline is the solution
Coalition formation through motivation and trust
No full textCooperation is the fundamental underpinning of multi-agent systems, allowing agents to interact to achieve their goals. Where agents are self-interested, or potentially unreliable, there must be appropriate mechanisms to cope with the uncertainty that arises. In particular, agents must manage the risk associated with interacting with others who have different objectives, or who may fail to fulfil their commitments. Previous work has utilised the notions of motivation and trust in engendering successful cooperation between self-interested agents. Motivations provide a means for representing and reasoning about agents’ overall objectives, and trust offers a mechanism for modelling and reasoning about reliability, honesty, veracity and so forth. This paper extends that work to address some of its limitations. In particular, we introduce the concept of a clan: a group of agents who trust each other and have similar objectives. Clan members treat each other favourably when making private decisions about cooperation, in order to gain mutual benefit. We describe mechanisms for agents to form, maintain, and dissolve clans in accordance with their self-interested nature, along with giving details of how clan membership influences individual decision making. Finally, through some simulation experiments we illustrate the effectiveness of clan formation in addressing some of the inherent problems with cooperation among self-interested agents
A Motivation Based Planning and Execution Framework
No full textAI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection
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