This paper presents a methodology for agent-oriented analysis and design. The methodology is 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. The methodology is founded on the view of a system as a computational organisation consisting of various interacting roles. We illustrate the methodology through a case study (an agent-based business process management system).

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.

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 de nes a range of strategies and tactics that agents can employ to generate initial o ers, evaluate proposals and o er counter proposals. The model is based on computationally tractable assumptions, demonstrated in the domain of business process management and empirically evaluated. Keywords: Multi-agent systems, Negotiation, Business Process Management 1

Abstract. Many autonomous agents operate in domains in which the cooperation of their fellow agents cannot be guaranteed. In such domains negotiation is essential to persuade others of the value of co-operation. This paper describes a general framework for negotiation in which agents exchange proposals backed by arguments which summarise the reasons why the proposals should be accepted. The argumentation is persuasive because the exchanges are able to alter the mental state of the agents involved. The framework is inspired by our work in the domain of business process management and is explained using examples from that domain. Keywords: Automated negotiation, Argumentation, Persuasion. 1

Abstract. 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 and is demonstrated in the domain of business process management. Initial proofs about the convergence of negotiation are also presented. 1

widespread, standardisation of ontologies, message content and message protocols will be necessary. In this paper, we present a lifecycle of a business-to-business e-commerce interaction, and show how the Semantic Web can support a service description language that can be used throughout this lifecycle. By using DAML+OIL, we develop a service description language su#ciently expressive and flexible to be used not only in advertisements, but also in matchmaking queries, negotiation proposals and agreements. We also identify which operations must be carried out on this description language if the B2B lifecycle is to be fully supported. We do not propose specific standard protocols, but instead argue that our operators are able to support a wide variety of interaction protocols, and so will be fundamental irrespective of which protocols are finally adopted.

This paper presents a high-level framework for analysing and designing intelligent agents. The framework's key abstraction mechanism is a new computer level called the Social Level. The Social Level sits immediately above the Knowledge Level, as defined by Allen Newell, and is concerned with the inherently social aspects of multiple agent systems. To illustrate the working of this framework, an important new class of agent is identified and then specified. Socially responsible agents retain their local autonomy but still draw from, and provide resources to, the larger community. Through empirical evaluation, it is shown that such agents produce both good system-wide performance and good individual performance. 1. INTRODUCTION The number of multi-agent systems being designed and built is rapidly increasing as software agents gain acceptance as a powerful and useful technology for solving complex problems (Chaib-draa, 1995; Jennings, 1994; PAAM, 1996). As applications become more comple...

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 modelling, designing, and implementing complex systems. Yet, to date, there has been little systematic analysis of what makes an agent such an appealing and powerful conceptual model. Moreover, even less effort has been devoted to exploring 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, highlevel social interactions, and that can operate within flexible organisational structures. 1

This paper analyzes automated distributive negotiation where agents have firm deadlines that are private information. The agents are allowed to make and accept offers in any order in continuous time. We show that the only sequential equilibrium outcome is one where the agents wait until the first deadline, at which point that agent concedes everything to the other. This holds for pure and mixed strategies. So, interestingly, rational agents can never agree to a nontrivial split because offers signal enough weakness of bargaining power (early deadline) so that the recipient should never accept. Similarly, the offerer knows that it offered too much if the offer gets accepted: the offerer could have done better by out-waiting the opponent. In most cases, the deadline effect completely overrides time discounting and risk aversion: an agent's payoff does not change with its discount factor or risk attitude. Several implications for the design of negotiating agents are discussed. We also present an effective protocol that implements the equilibrium outcome in dominant strategies.

This paper describes the KRAFT architecture which supports the fusion of knowledge from multiple, distributed, heterogeneous sources. The architecture uses constraints as a common knowledge interchange format, expressed against a common ontology. Knowledge held in local sources can be tranformed into the common constraint language, and fused with knowledge from other sources. The fused knowledge is then used to solve some problem or deliver some information to a user. Problem-solving in KRAFT typically exploits pre-existing constraint solvers. KRAFT uses an open and flexible agent architecture in which knowledge sources, knowledge fusing entities, and users are all represented by independent KRAFT agents, communicating using a messaging protocol. Facilitator agents perform matchmaking and brokerage services between the various kinds of agent. KRAFT is