多智能体网络的混杂建模与量化一致性

针对多智能体网络系统中普遍存在的拓扑变化、信息量化问题,综合考虑了状态量化和拓扑切换的影响.基于一种新的量化器构建了量化反馈作用下多智能体系统的混杂模型,并进一步分析了该混杂系统的有限时间收敛性,给出了一致收敛的时间下界.最后,对不同切换网络拓扑进行计算机仿真,验证所得结果的有效性.     

Research of Multiagent Coordination and Cooperation Algorithm

To solve the problem of conflict and deadlock with agents in multiagent system, an algorithm of multiagent coordination and cooperation was proposed. Taking agent in multiagent system as a player, the pursuit problem Markov model was built. The solution was introduced to get the optimal Nash equilibrium by multiagent reinforcement learning. The method of probability and statistics and Bayes formula was used to estimate the policy knowledge of other players. Relative mean deviation method was used to evaluate the confidence degree in order to increase the convergence speed. The simulation results on pursuit problem showed the feasibility and validity of the given algorithm.     

Congregation Formation in Multiagent Systems

We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs in large-scale MAS. When agents must search for other agents to interact with, congregations provide a way for agents to bias this search towards groups of agents that have tended to produce successful interactions in the past. This causes each agent's search problem to scale with the size of a congregation rather than the size of the population as a whole. In this paper, we present a formal model of a congregation and then apply Vidal and Durfee's CLRI framework [24] to the congregating problem. We apply congregating to the affinity group domain, and show that if agents are unable to describe congregations to each other, the problem of forming optimal congregations grows exponentially with the number of agents. The introduction of labelers provides a means of coordinating agent decisions, thereby reducing the problem's complexity. We then show how a structured label space can be exploited to simplify the labeler's decision problem and make the congregating problem linear in the number of labels. We then present experimental evidence demonstrating how congregating can be used to reduce agents' search costs, thereby allowing the system to scale up. We conclude with a comparison to other methods for coordinating multiagent behavior, particularly teams and coalitions.     

GEAMAS: A Generic Architecture for Agent-Oriented Simulations of Complex Processes

This paper's object is to present the results of the GEAMAS project which aims at modeling and simulating natural complex systems. GEAMAS is a generic architecture of agents used to study the behavior emergence in such systems. It is a multiagent program meant to develop simulation applications. Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agent-oriented approach, where interactions lead to a global behavior. This approach helps in understanding how non-determinist behavior can emerge from interactions between agents, which is near of self-organized criticality used to explain natural phenomena. In the Applied Artificial Intelligence context, this paper presents an agent software architecture using a model of agent. This architecture is composed of three abstract levels over which the complexity is distributed and reduced. The architecture is implemented in ReActalk, an open agent-oriented development tool, which was developed on top of Smalltalk-80. To illustrate our purpose and to validate the architecture, a simulation program to help in predicting volcanic eruptions was investigated. This program was run over a period of one year and has given many satisfying results unattainable up to there with more classical approaches.     

Conjectural Equilibrium in Multiagent Learning

Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment effectively changes. Moreover, other agents' actions are often not directly observable, and the actions taken by the learning agent can strongly bias which range of behaviors are encountered. We define the concept of a conjectural equilibrium, where all agents' expectations are realized, and each agent responds optimally to its expectations. We present a generic multiagent exchange situation, in which competitive behavior constitutes a conjectural equilibrium. We then introduce an agent that executes a more sophisticated strategic learning strategy, building a model of the response of other agents. We find that the system reliably converges to a conjectural equilibrium, but that the final result achieved is highly sensitive to initial belief. In essence, the strategic learner's actions tend to fulfill its expectations. Depending on the starting point, the agent may be better or worse off than had it not attempted to learn a model of the other agents at all.     

钢铁工业过程多智能体系统研究

结合钢铁工业过程的特点，通过分析冶金过程工业智能控制的发展状况，提出了采用多Ａｇｅｎｔ技术，建立钢铁工业多智能体系统，从而实现钢铁工业的分布式智能控制，分析了系统建立的必要性和可行性，并给出了该系统的结构和实现方法。     

基于Petri网的实时多智能体系统建模

给出基于Petri网的实时多Agent系统建模方法，它通过Petri网建立由接口模块、目标模块、计划模块、调度模块、知识库模块、环境模块、内部模块和控制模块组成的实时Agent模型，抽象和清晰地描述出实时Agent内部和外部特征。     

火电厂多代理控制系统

针对火电厂非线性、多变量和多控制目标的特点,设计了一个火电厂多代理控制系统(PPMACS).在PP MACS中,前馈控制代理(FFCAs)采用神经模糊系统进行决策,反馈控制代理(FBCAs)采用基于遗传算法的模糊系统进行决策.优化任务分解代理(OTDAs)通过一个优化代理和一个分解代理来进行多目标优化分解PPMACS的任务.协调代理根据运行条件协调PPMACS的各个代理.仿真结果显示了火电厂多代理控制系统能够实现火电单元机组的多目标运行和大范围负荷跟踪.神经网络、模糊逻辑和遗传算法是PPMACS中的智能代理进行决策的有效工具.     

Negotiation Agents that Make Prudent Compromises and are Slightly Flexible in Reaching Consensus

The contribution of this work is designing and developing enhanced market-driven agents with the flexibility to (1) respond to changing market conditions, and (2) raise and relax trade aspirations. Previous theoretical analyses have shown that market-driven agents ( MDA s) make prudent compromises by reacting to changing market situations by taking into account factors such as competition, deadlines, and trading options. This work augments the design of an MDA with three fuzzy decision controllers that guide the agent in (i) relaxing trade aspiration in face of intense negotiation pressure, and (ii) raising trade aspiration in extremely favorable markets. Results from extensive simulations conducted using an implemented testbed suggest that when compared to MDA s, agents in this work achieved (1) higher success rates in reaching deals, (2) higher average utilities, and (3) higher expected utility.     

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system.