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.

Introduction Agents operate and exist in some environment, which typically is both computational and physical. The environment might be open or closed, and it might or might not contain other agents. Although there are situations where an agent can operate usefully by itself, the increasing interconnection and networking of computers is making such situations rare, and in the usual state of affairs the agent interacts with other agents. Whereas the previous chapter defined the structure and characteristics of an individual agent, the focus of this chapter is on systems with multiple agents. At times, the number of agents may be too numerous to deal with them individually, and it is then more convenient to deal with them collectively, as a society of agents. In this chapter, we will learn how to analyze, describe, and design environments in which agents can operate effectively and interact with each other productively. The environments will provide a computational infrastructu

this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A filtering system, SIFTER, has been implemented based on the model, using established techniques in information retrieval and artificial intelligence. These techniques include document representation by a vector-space model, document classification by unsupervised learning, and user modeling by reinforcement learning. The system can filter information based on content and a user's specific interests. The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents. We also describe experimental studies conducted with SIFTER to filter computer and information science documents collected from the Internet and commercial database services. The experimental results demonstrate that the system performs very well in filtering documents in a realistic problem setting.

The complexity of the modern information carrying landscape requires a sophisticated view in which information is acquired rather than simply retrieved; where the process must be dynamic, incremental, and constrained by resource limitations. In this paper, we describe a multi-agent architecture called MACRON based on the Cooperative Information Gathering (CIG) paradigm designed specifically for such complex information gathering environments. Top level user queries drive the creation of partially elaborated information gathering plans, resulting in the employment of multiple cooperative agents for the purpose of achieving goals and subgoals within those plans. Agents in MACRON incorporate capabilities to exploit subproblem interdependencies, manage uncertainty inherent in multi-agent search, intelligently trade-off solution quality for resource limitations and exploit or avoid redundancy as needed. 1 Introduction The complexity of the modern information carrying landscape requires a s...

Most of the existing research in peer-to-peer systems focuses on protocol design and doesn't consider the rationality of each peer. One phenomenon that should not be ignored is free riding. Some peers simply consume system resources but contribute nothing to the system. In this paper we present an agentbased peer-to-peer system, in which each peer is a software agent and the agents cooperate to search the whole system through referrals. We present a static and a dynamic pricing mechanism to motivate each agent to behave rationally while still achieving good overall system performance. We study the behavior of the agents under two pricing mechanisms and evaluate the impact of free riding using simulations.

Abstract. The task in expert finding is to identify members of an organization with relevant expertise on a given topic. In existing expert finding systems, profiles are constructed from sources such as email or documents, and used as the basis for expert identification. In this paper, we leverage the organizational hierarchy (depicting relationships between managers, subordinates, and peers) to find members for whom we have little or no information. We propose an algorithm to improve expert finding performance by considering not only the expertise of the member, but also the expertise of his or her neighbors. We show that providing this additional information to an expert finding system improves its retrieval performance.

Abstract. Most of the existing research in peer-to-peer systems focuses on protocol design and doesn’t consider the rationality of each peer. One phenomenon that should not be ignored is free riding. Some peers simply consume system resources but contribute nothing to the system. In this paper we present an agentbased peer-to-peer system, in which each peer is a software agent and the agents cooperate to search the whole system through referrals. We present a static and a dynamic pricing mechanism to motivate each agent to behave rationally while still achieving good overall system performance. We study the behavior of the agents under two pricing mechanisms and evaluate the impact of free riding using simulations. 1

This paper uses the term “service ” for a service instance, and the term “agent ” for an agent who provides or consumes a service. We consider real-life or business services (e.g., business process outsourcing, software development service involving human experts).