计算机支持协同式水平井设计系统

运用计算机支持协同工作的最新技术,面向石油钻井中水平井设计过程,研究开发了基于CSCW的协同式水平井设计软件系统,建立由计算机网络支持的多智能体水平井协作设计软件应用平台环境,利用网络通信和移动计算技术来实现动态水平井设计信息的多方传输和并行设计。     

Cooperative behavior based on a subjective map with shared information in a dynamic environment

This paper proposes a subjective map representation that enables a robot in a multi-agent system to make decisions in a dynamic, hostile environment. A typical situation can be found in the Sony four-legged robot league of the RoboCup competition. The subjective map is a map of the environment that each agent maintains regardless of the objective consistency of the representation among the agents. Due to the map's subjectivity, it is not affected by incorrect information acquired by other agents. The method is compared with conventional methods with or without information sharing.     

A preference processing model for cooperative agents

When multiple valid solutions are available to a problem, preferences can be used to indicate a choice. In a distributed system, such a preference-based solution can be produced autonomous agents cooperating together, but the attempt will lead to contention if the same resource is given preference by several user-agents. To resolve such contentions, this paper proposes a market-based payment scheme for selling and buying preferences by the contenders, in which the best solution is defined as the one where as many preferences as theoretically possible are globally met. After exploring the nature of preference, the paper develops a preference processing model based on the market based scheme, and presents a theoretical performance model to verify the correctness of the processing model. This verification is provided by a simulation study of the processing model. For the simulation study, a manufacturing environment is conjectured, where a set of tasks are resolved into subtasks by coordinator agents, and then these subtasks are allocated to assembler agents through cooperation and negotiation, in which preferred resources are exchanged against payments. The study shows that our agent based strategy not only produces convergence on the total preference value for the whole system, but also reaches that final value irrespective of the initial orderof subtask allocation to the assemblers.     

Agent协作中的一种选择算法的实现

多Agent系统是一种协同工作系统,多Agent系统在完成任务时所选择的任务组织者对任务完成质量的成坏至关重要。在给出一个多Agent系统组织模型的基础上,提出了一种选择算法,该算法在选择合作时既考虑了Agent完成任务的历史情况,又考虑了Agent能力的变化,从而保证了完成任务的成功率和系统灵活性。     

Towards a theory of delegation for agent-based systems

In this paper a theory of delegation is presented. There are at least three reasons for developing such a theory. First, one of the most relevant notions of “agent” is based on the notion of “task” and of “on behalf of”. In order to found this notion a theory of delegation among agents is needed. Second, the notion of autonomy should be based on different kinds and levels of delegation. Third, the entire theory of cooperation and collaboration requires the definition of the two complementary attitudes of goal delegation and adoption linking collaborating agents.

After motivating the necessity for a principled theory of delegation (and adoption) the paper presents a plan-based approach to this theory. We analyze several dimensions of the delegation/adoption (on the basis of the interaction between the agents, of the specification of the task, of the possibility to subdelegate, of the delegation of the control, of the help levels). The agent's autonomy and levels of agency are then deduced. We describe the modelling of the client from the contractor's point of view and vice versa, with their differences, and the notion of trust that directly derives from this modelling.

Finally, a series of possible conflicts between client and contractor are considered: in particular collaborative conflicts, which stem from the contractor's intention to help the client beyond its request or delegation and to exploit its own knowledge and intelligence (reasoning, problem solving, planning, and decision skills) for the client itself.     

一种不完全信息下的多智能体协作机制

在分布式多智能体系统中,由于agen嗵常具有不完全信息,因此大大影响了系统的协作效果。该文采用博弈论和决策理论相结合的方法,将不完全信息下的agcnt协作转换成完全信息下agent协作问题来求解,提出了一种不完全信息下的agent协作机制。     

Cooperative behavior acquisition for mobile robots in dynamically changing real worlds via vision-based reinforcement learning and development

In this paper, we first discuss the meaning of physical embodiment and the complexity of the environment in the context of multi-agent learning. We then propose a vision-based reinforcement learning method that acquires cooperative behaviors in a dynamic environment. We use the robot soccer game initiated by RoboCup (Kitano et al., 1997) to illustrate the effectiveness of our method. Each agent works with other team members to achieve a common goal against opponents. Our method estimates the relationships between a learner's behaviors and those of other agents in the environment through interactions (observations and actions) using a technique from system identification. In order to identify the model of each agent, Akaike's Information Criterion is applied to the results of Canonical Variate Analysis to clarify the relationship between the observed data in terms of actions and future observations. Next, reinforcement learning based on the estimated state vectors is performed to obtain the optimal behavior policy. The proposed method is applied to a soccer playing situation. The method successfully models a rolling ball and other moving agents and acquires the learner's behaviors. Computer simulations and real experiments are shown and a discussion is given.     

基于MAS的专家系统开发工具

结合分布式人工智能理论和面向对象及面向智能体技术,给出基于MAS（Multi0agent System)的专家系统开发工具的系统结构和设计,并描述了系统中的知识通信语言和协作机制。采用Java语言,在分布式网络环境下,实现了该工具的原型系统。用该工具开发了一个基于MAS的协作中医诊断专家系统,运行效果良好。     

Realization of emergent behavior in collective autonomous mobile agents using an artificial neural network and a genetic algorithm

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents emergent behavior during simulation.This paper was supported by WonKwang University in 2004.     

Adaptive selection of reactive/deliberate planning for a dynamic environment

This paper proposes and evaluates a new real-time reactive planning approach for a dynamic environment. In addition to having the features of conventional real-time reactive planning, which can react in a dynamic environment, our planning can perform deliberate planning appropriately. The proposed planning uses three kinds of agents: behavior agents that control simple behavior, planning agents that make plans to achieve their own goals, and behavior-selection agents that intermediate between behavior agents and planning agents. They coordinate a plan in an emergent way for the planning system as a whole. We confirmed the effectiveness of our planning by means of a simulation. Furthermore, we implemented an active-vision system and used it to verify the real-world efficiency of our planning.     

基于管理Agent协作模型的研究

多Agent协作求解是分布式人工智能要研究的基本问题。该文基于管理agent概念提出一个新的协作模型，该模型利用管理Agent对多Agent系统进行全局协作分配，协作申请分级处理，解决了传统协作模型中存在的模型与应用领域有关和只适用于静态环境的问题。     

多智能体协作的两层强化学习实现方法

提出了多智能体协作的两层强化学习方法。该方法主要通过在单个智能体中构筑两层强化学习单元来实现,将该方法应用于3个智能体协作抬起圆形物体的计算机模拟中,结果表明比采用传统强化学习方法的智能体协作得更好。     

Modeling dynamic environments in multi-agent simulation

Real environments in which agents operate are inherently dynamic—the environment changes beyond the agents’ control. We advocate that, for multi-agent simulation, this dynamism must be modeled explicitly as part of the simulated environment, preferably using concepts and constructs that relate to the real world. In this paper, we describe such concepts and constructs, and we provide a formal framework to unambiguously specify their relations and meaning. We apply the formal framework to model a dynamic RoboCup Soccer environment and elaborate on how the framework poses new challenges for exploring the modeling of environments in multi-agent simulation.     

Market-aware agents for a multiagent world

A computational market is any collection of software agents interacting through a price system. Markets can provide effective allocation of resources for a variety of distributed environments, and economic analysis is a powerful design tool for interaction mechanisms. The spread of computational markets puts a premium on market-aware agents, and presents a case for market awareness on the part of agent developers and AI researchers as well.     

面向装配的多Agent协同设计研究

分析了面向装配设计（ＤＦＡ）技术发展和存在的问题,提出了ＤＦＡ中多Ａｇｅｎｔ协同设计研究的必要性。对设计Ａｇｅｎｔ的概念、结构及设计过程进行了描述和研究,并在此基础上对多Ａｇｅｎｔ协同设计过程和表示做了进一步的研究。最后,对面向ＤＦＡ的多Ａｇｅｎｔ协同原型设计系统进行了设计。     

Time-bound negotiation framework for electronic commerce agents

For efficient and informative coordination of agents especially in electronic commerce environment, a time-bound agent negotiation framework is proposed utilizing a time-based commitment scheme. By attaching commitment duration to agent messages, the traditional contract net protocol is extended to a time-bound negotiation framework (TBNF). The proposed negotiation framework has a new message type which allows for parties to agree upon the extension of a commitment duration, and a novel commitment concept in the form of negative commitment. The semantics of the messages with the commitment duration are interpreted, and then the three typical negotiation protocols are formally defined and compared — nothing-guaranteed protocol, acceptance-guaranteed protocol, and finite-time guarantee protocol — which can be incorporated into TBNF. The TBNF should provide a background for efficient and effective electronic commerce negotiation while accommodating each agent's adaptive negotiation strategy.     

协同设计环境下的任务分配方法的研究

介绍一种基于辅助Agent的合同网模型的任务分配方法,并给出了协作过程中的任务选择策略和Agent选择策略,改进基本合同网模型的缺点和不足,以解决多Agent的协同设计系统中分布合作求解问题和任务分配,达到全局最优化,产生高质量的设计及有效的资源利用,并减少协商时间和信息流量,提高系统效率。     

人工智能中的合作分布式规划

人工智能中的合作分布式规划是分布式问题求解的一个特例,同时又具有规划问题的特征,其应用范围广,研究意义大。文章对该领域近来的发展进行了总结,以规划任务表达和分解、任务分派、任务完成为主线,给出已有的研究成果、目前的研究现状,并着重讨论了任务完成阶段运用的各种协调技术,文末还指出未来的研究方向。