A Verification Framework for Agent Communication

In this paper, we introduce a verification method for the correctness of multiagent systems as described in the framework of ACPL (Agent Communication Programming Language). The computational model of ACPL consists of an integration of the two different paradigms of CCP (Concurrent Constraint Programming) and CSP (Communicating Sequential Processes). The constraint programming techniques are used to represent and process information, whereas the communication mechanism of ACPL is described in terms of the synchronous handshaking mechanism of CSP. Consequently, we show how to define a verification method for ACPL in terms of an integration of the verification methods for CCP and CSP. We prove formally the soundness of the method and discuss its completeness.     

一种基于模板分析的Agent信息交换方法

提出一种基于模板分析方法,说明Agent之间通信如何使用这种方法去构造和分解消息,并说明这种方法减少了在ACL上的假设和消息格式的使用。模板的应用降低了Agent之间相互操作的需求,允许Agent在一个开放的多Agent系统中通信,可以事先不考虑缺少ACL协议和消息格式要求。     

Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain

This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.     

A Negotiation Enabling Agent Based Infrastructure: Composition and Behavior

Software agent-based negotiation is a major method to automate the interactions in electronic marketplaces and Internet enabled communities. The traditional approach is to let the agents to interact directly. In this paper it has been investigated how a mediator agent can improve the chances to reach the agreement via bargaining. Although the ideal mathematical model was proposed in the seventies, this was never implemented as a working mechanism, due to the fact that the mediator needed information that was difficult to gather and the usual environment was not repetitive enough to consolidate this information for a fair mediation. The agent-based infrastructure proposed collects continuously data about the negotiating parties and the mediator agents use this data to reduce the exaggeration of the parties. The paper includes a mediation example and the major conclusion is that negotiation is improved by a mediator which has historical data about the negotiating parties.     

一个具有高安全性的移动Agent系统模板结构

在分析现有的移动Agent系统的特点以及MASIF(mobile agent system interoperability facility)规范的基础上,给出了一个具有高安全性的移动Agent系统Jamogents及其模板结构,描述了其工作流程,并在重载Java类java.lang.SecurityManager的基础上实现了一种用于加密和数字签名的算法RIM(RSA+IDEA+MD5).     

Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture

The development of enabling infrastructure for the next generation of multi-agent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multi-agent community.Current infrastructure support does not materially assist in the development of sophisticated agent coordination strategies. It is the need for and the development of such a high-level support structure that will be the focus of this paper. A domain-independent (generic) agent architecture is proposed that wraps around an agent's problem-solving component in order to make problem solving responsive to real-time constraints, available network resources, and the need to coordinate—both in the large and small—with problem-solving activities of other agents. This architecture contains five components, local agent scheduling, multi-agent coordination, organizational design, detection and diagnosis, and on-line learning, that are designed to interact so that a range of different situation-specific coordination strategies can be implemented and adapted as the situation evolves. The presentation of this architecture is followed by a more detailed discussion on the interaction among these components and the research questions that need to be answered to understand the appropriateness of this architecture for the next generation of multi-agent systems.     

基于Agent的决策支持系统的构建

将人工智能中的最新技术Agent技术用于构建决策支持系统 ,引入交互Agent、信息Agent、模型Agent和知识A gent来构成系统的智能部分 ,利用多个Agent的通讯与合作 ,更好地帮助用户进行决策 ;同时给出了各类Agent的具体实现方法     

基于π演算的一种Agent组织模型

Agent组织是多Agent系统研究的重要方面。从直观含义出发，将组织看做Agent之间的控制关系，以π演算为工具，给出了组织角色的定义和根据组织框架构建具体组织的过程。角色和组织框架都用进程表示，确定了Agent加入和脱离组织的严格描述，相比Ferber等以π演算和化学自动机定义组织形成过程的形式语义的工作，本文显式定义了角色和组织框架，所讨论的组织更具一般性。     

Pricing in Agent Economies Using Multi-Agent Q-Learning

This paper investigates how adaptive software agents may utilize reinforcement learning algorithms such as Q-learning to make economic decisions such as setting prices in a competitive marketplace. For a single adaptive agent facing fixed-strategy opponents, ordinary Q-learning is guaranteed to find the optimal policy. However, for a population of agents each trying to adapt in the presence of other adaptive agents, the problem becomes non-stationary and history dependent, and it is not known whether any global convergence will be obtained, and if so, whether such solutions will be optimal. In this paper, we study simultaneous Q-learning by two competing seller agents in three moderately realistic economic models. This is the simplest case in which interesting multi-agent phenomena can occur, and the state space is small enough so that lookup tables can be used to represent the Q-functions. We find that, despite the lack of theoretical guarantees, simultaneous convergence to self-consistent optimal solutions is obtained in each model, at least for small values of the discount parameter. In some cases, exact or approximate convergence is also found even at large discount parameters. We show how the Q-derived policies increase profitability and damp out or eliminate cyclic price wars compared to simpler policies based on zero lookahead or short-term lookahead. In one of the models (the Shopbot model) where the sellers' profit functions are symmetric, we find that Q-learning can produce either symmetric or broken-symmetry policies, depending on the discount parameter and on initial conditions.