多智能体车间调度系统研究

现在制造业所面临的动态需求使得其必须具有更加灵活的应变机制，这使得车间调度问题变得越来越复杂。本文采取多智能体系统技术（MAS）设计了一个包含四个智能体（agent）的多智能体车间调度系统，分别为车间调度智能体、任务分配智能体、车间资源智能体以及拍卖智能体。通过这四个智能体的通信、交互和合作，系统可以给出一个满足当前制造需求的调度最优结果。     

基于DCOM的动态运输调度多智能体系统研究*

针对物流运输调度中的客户需求动态性和随机性问题的解决,设计了一种基于DCOM的动态运输调度多智能体系统。在多智能体系统中,设计了包括预规划智能体、实时监控智能体、决策智能体和车辆智能体四类智能体。智能体以DCOM式组件形式实现,智能体之间的通信和协调由决策智能体集中执行。通过一个实例验证了系统算法的有效性,同时也为动态运输调度问题的解决提供了一个可参考的思路。     

面向智能体逻辑程序设计系统GKD－AOLPS

我们在面向对象逻辑程序设计系统ＧＫＤ－ＯＯＰＳ的基础上，研究并实现了面向智能体逻辑程序设计系统ＧＫＤ－ＡＯＬＰＳ。本文首先提出了智能体的结构和机制，然后给出了一种智能体的元级结构；最后根据言语行为理论，研究了ＧＫＤ－ＡＯＬＰＳ系统所提供的智能体通信原语的语用性质。     

生产调度多智能体仿真系统研究

针对生产调度系统进行深入分析,以高层架构体系为体系架构,运用多智能体技术,建立系统模型,具体的调度算法或规则能内化成智能体的行为方式。该系统以Jade及Jess为主开发工具,为生产调度系统提供了一个灵活、通用、可扩展性强的仿真平台。     

电子商务中的自动协商

描述电子商务中基于智能体的自动协商，给出基于智能体的自动协商模型和协商智能体的体系结构，描述了一组构成协商协议的通信原语。通过约束满足机制、协商策略和提议评估算法实现自动协商。并实现了一个供应链管理中买卖自动协商的原型系统。     

面向智能体逻辑程序设计系统GKD—AOLPS

我们在面向对象逻辑程序设计系统ＧＫＤ－ＯＯＰＳ的基础上，研究并实现了面向智能体逻辑程序设计系统ＧＫＤ－ＡＯＬＰＳ。本文首先提出了智能体的结构和机制，然后给出了一种智能体的元级结构，最后根据言语行为理论，研究了ＧＫＤ－ＡＯＬＰＳ系统所提供的智能体通信原语的语用性质。     

多智能体运输调度系统协商机制的研究

针对大型企业运输调度问题,采用多智能体技术,提出了一个多智能体运输调度系统模型,将汽车资源构造为具有自主能力的汽车智能体,从而提高了系统问题求解的能力,并对系统中多智能体之间的协商机制进行了探讨。     

基于多Agent柔性制造单元的生产调度研究

针对基于制造单元的作业车间的生产调度问题进行了研究,结合多代理的智能性、灵活性和遗传算法的智能优化能力,建立基于多智能体的柔性制造单元的作业车间的调度系统模型.然后,提出了集成多智能体和遗传算法的动态调度策略和调度协商机制;最后,应用此方法完成了常规调度和异常调度的仿真算例.结果表明所开发系统可以解决基于加工单元的制造...     

FMS多智能体调度系统的角色建模

为了实现FMS多智能体调度系统,提出一种基于角色的系统体系结构。根据该结构的逻辑过程设计出调度系统的招标委员会流程角色模型,并对每个智能体进行角色分配,定义其工作方式与数据访问方式。通过角色的泛化和聚合,使得系统中的智能体协同能力和适应能力更强。     

基于多智能体的流程工业动态调度研究

根据流程工业某车间的生产过程,建立基于多智能体的生产调度系统模型,分析各智能体之间的关系,利用多智能体之间基于博弈论的协商机制,提出一个双边单议题多阶段的谈判模型,解决以工序流量或产品产量为目标的调度问题。对某隔膜烧碱生产线调度实例进行仿真,结果验证了该系统的可行性和有效性。     

Toward a programming theory for rational agents

An important problem in agent verification is a lack of proper understanding of the relation between agent programs on the one hand and agent logics on the other. Understanding this relation would help to establish that an agent programming language is both conceptually well-founded and well-behaved, as well as yield a way to reason about agent programs by means of agent logics. As a step toward bridging this gap, we study several issues that need to be resolved in order to establish a precise mathematical relation between a modal agent logic and an agent programming language specified by means of an operational semantics. In this paper, we present an agent programming theory that provides both an agent programming language as well as a corresponding agent verification logic to verify agent programs. The theory is developed in stages to show, first, how a modal semantics can be grounded in a state-based semantics, and, second, how denotational semantics can be used to define the mathematical relation connecting the logic and agent programming language. Additionally, it is shown how to integrate declarative goals and add precompiled plans to the programming theory. In particular, we discuss the use of the concept of higher-order goals in our theory. Other issues such as a complete axiomatization and the complexity of decision procedures for the verification logic are not the focus of this paper and remain for future investigation. Part of this research was carried out while the first author was affiliated with the Nijmegen Institute for Cognition and Information, Radboud University Nijmegen.     

Agent Programming in 3APL

An intriguing and relatively new metaphor in the programming community is that of an intelligent agent. The idea is to view programs as intelligent agents acting on our behalf. By using the metaphor of intelligent agents the programmer views programs as entities which have a mental state consisting of beliefs and goals. The computational behaviour of an agent is explained in terms of the decisions the agent makes on the basis of its mental state. It is assumed that this way of looking at programs may enhance the design and development of complex computational systems.To support this new style of programming, we propose the agent programming language 3APL. 3APL has a clear and formally defined semantics. The operational semantics of the language is defined by means of transition systems. 3APL is a combination of imperative and logic programming. From imperative programming the language inherits the full range of regular programming constructs, including recursive procedures, and a notion of state-based computation. States of agents, however, are belief or knowledge bases, which are different from the usual variable assignments of imperative programming. From logic programming, the language inherits the proof as computation model as a basic means of computation for querying the belief base of an agent. These features are well-understood and provide a solid basis for a structured agent programming language. Moreover, on top of that 3APL agents use so-called practical reasoning rules which extend the familiar recursive rules of imperative programming in several ways. Practical reasoning rules can be used to monitor and revise the goals of an agent, and provide an agent with reflective capabilities.Applying the metaphor of intelligent agents means taking a design stance. From this perspective, a program is taken as an entity with a mental state, which acts pro-actively and reactively, and has reflective capabilities. We illustrate how the metaphor of intelligent agents is supported by the programming language. We also discuss the design of control structures for rule-based agent languages. A control structure provides a solution to the problem of which goals and which rules an agent should select. We provide a concrete and intuitive ordering on the practical reasoning rules on which such a selection mechanism can be based. The ordering is based on the metaphor of intelligent agents. Furthermore, we provide a language with a formal semantics for programming control structures. The main idea is not to integrate this language into the agent language itself, but to provide the facilities for programming control structures at a meta level. The operational semantics is accordingly specified at the meta level, by means of a meta transition system.     

Effect of symmetry bias on linguistic evolution

As described in this paper, we investigated the effect of the symmetry bias on linguistic evolution. We specifically examined symmetry bias, which indicates the meaning in a state of environment. For this task, we constructed a meaning selection iterated learning model based on Simon Kirby’s iterated learning Model, and used it for simulation with three strategies: perfect matching symmetry bias, imperfect matching symmetry bias, and random strategy. Results of applying imperfect matching symmetry bias show that the language of the agent evolved into more compositional language. The agent acquired a more expressive, and a more similar language to the parent’s language than with the Random strategy agent. However, application of perfect matching symmetry bias showed that the language of the agent did not evolve. The agent acquired a less expressive and a more different language to the parent’s language than with Random strategy agent. Our experimentally obtained results demonstrate that the effect of imperfect matching symmetry bias accelerates linguistic evolution into compositional language, whereas perfect matching symmetry bias disturbs linguistic evolution.     

Agent规范与编程语言SPLAW的语法和语义

本文描述了Ａｇｅｎｔ规范与编程语言ＳＰＬＡＷ的语法，为ＳＰＬＡＷ中的各种语言成分提供了操作语义，并基于标记转换系统给出Ａｇｅｎｔ性质的证明规则。由于ＳＰＬＡＷ的通信部分采用不依赖于具体应用的Ａｇｅｎｔ交互语言ＫＱＭＬ，使遵循同一标准的Ａｇｅｎｔ之间能进行协作问题求解。ＳＰＬＡＷ首次为Ａｇｅｎｔ提供继承支持，使其可以实现具有面向对象特征的多线程系统。ＳＰＬＡＷ屏了为语言引入内部模态词的缺陷，试图从外     

一种面向用户的Agent交互描述语言

Agent的应用最终是面向普通用户的,所以为了促进普通用户对agent的使用和理解,设计和实现了Q语言。该文首先介绍了Q语言的设计目的,执行机理,及基本的语法构件。在此基础上,通过一个应用说明了如何利用Q语言来设计用户和agent之间的交互。     

Ontology-based computational intelligent multi-agent and its application to CMMI assessment

This study presents an ontology-based computational intelligent multi-agent system for Capability Maturity Model Integration (CMMI) assessment. An ontology model is developed to represent the CMMI domain knowledge that will be adopted by the computational intelligent multi-agent. The CMMI ontology is predefined by domain experts, and created by the ontology generating system. The computational intelligent multi-agent comprises a natural language processing agent, an ontological reasoning agent and a summary agent. The multi-agent deals with the evaluation reports from the natural language processing agent, infers the term relation strength between the ontology and the evaluation report, and then summarizes the main sentences of the evaluation report. The summary reports are meanwhile transmitted back to the domain expert, which makes the domain expert further adjust the CMMI ontology. Experimental results indicate that the ontology-based computational intelligent multi-agent can effectively summarize the evaluation reports for the CMMI assessment.     

Agent通信语言及相关理论的研究

在多代理系统里,为了实现某一目标,Agent需要通信和交互。因此,设计一个理想的Agent通信语言就显得十分重要。Agent通信语言的实现涉及的因素,除了语言本身外,还包括Agent理论、语义、证明和会话策略等。该文就Agent通信语言和相关理论作了简单的分析并指出存在的问题。     

OWL-QL—a language for deductive query answering on the Semantic Web

This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL query language (OWL-QL) as a candidate standard language and protocol for query–answering dialogues among Semantic Web computational agents using knowledge represented in the W3Cs ontology web language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query–answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute.     

多功能感知系统中的面向Agent技术

本文将人类语言分为自然语言和人体语言，阐述了人体语言的概念，讨论了人类语言的结构与包容关系，归纳了人体语言与自然语言的信息融合模型．在该模型的基础上，利用面向Ａｇｅｎｔ技术设计了一个多功能感知系统的框架结构，提出了Ａｇｅｎｔ关系图表示方法，并具体实现了一种人体语言感知原型系统．