智能Agent及其在网络搜索中的应用

本文介绍了Ａｇｅｎｔ及智能Ａｇｅｎｔ的概念、发展和应用领域，指明了智能Ａｇｅｎｔ的框架和实现技术，描述了智能Ａｇｅｎｔ在网络搜索中的应用，并对智能Ａｇｅｎｔ的前景做出了展望。     

并行工程基于Agent的集成框架研究

研究了并行工程基于Ａｇｅｎｔ的集成框架，提出了用于并行工程的多Ａｇｅｎｔ的基本模型和拓扑结构及Ａｇｅｎｔ间的通信协议，实现了异构Ａｇｅｎｔ之间的集成，这个多Ａｇｎｅｔ系统有效地支持了并行工程的工作过程。     

远程教学的Agent模型

利用网络进行远程教学有很多优点,本文讨论如何把Ａｇｅｎｔ技术应用到远程教学中,以增强教学效果,在多Ａｇｅｎｔ通信语言ＫＱＭＬ的基础上,提出了远程教学的Ａｇｅｎｔ模型,设计了Ａｇｅｎｔ的能力编码,通过对搜索教师Ａｇｅｎｔ算法的时间复杂度分析,说明在该模型中搜索教师Ａｇｅｎｔ是可行的和有效的。     

一种Agent结构的形式化模型

从外表看,Ａｇｅｎｔ是具有信念、意向、期望等意识属性的统一整体。Ａｇｅｎｔ之间可相互通信或相互合作,而且,Ａｇｅｎｔ自身还可以有诸如计算,推理或其他人们所希望的具有一定的智能特征的行为。为了把Ａｇｅｎｔ行为和它所应具有的内部结构联合起来,本文提出一种Ａｇｅｎｔ 形式化描述模型,在此模型的帮助下,可以较好地把一些Ａｇｅｎｔ的简单行为解释为Ａｇｅｎｔ内部状态的演变过程。这对Ａｇｅｎｔ的建模与实现,加深     

基于Agent模型的开发放超媒体个性化

该文提出了基于Ａｇｅｎｔ模型所实现的开放超媒体个性化，利用分布对象技术实现Ａｇｅｎｔ机制，并定义了多Ａｇｅｎｔ之间的通信的意义。利用这种Ａｇｅｎｔ机制实现了开放超媒体个性化的开发框架，可实现各个性化行为，以解决现有超媒体存在的问题。     

采用多Agent模型的协同编著系统的设计与实现

文中提出了一个基于多Ａｇｅｎｔ模型的协同编著系统，通过多Ａｇｅｎｔ之间的协作，支持分布文档数据的访问，复制，使系统具有较高的效率和鲁棒性，文中详细讨论了协同编著系统中多Ａｇｅｎｔ模型的特点，并在多Ａｇｅｎｔ通信，本文管理等方面给出具体实现。     

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     

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。     

智能Agent在Web发掘中的作用与实现

本文介绍了智能Ａｇｅｎｔ在Ｗｅｂ发掘中的作用，描述了智能Ａｇｅｎｔ的属性和结构，并讨论了一种构造Ｗｅｂ发掘Ａｇｅｎｔ的方案和它的学习方法。     

Internet上的软件Agent

本文首先介绍了Ａｇｅｎｔ的基本概念及其相关理论、体系结构的语言。然后较详细地介绍软件Ａｇｅｎｔ方面典型工作。最后，阐释Ｉｎｔｅｒｎｅｔ软件Ａｇｅｎｔ有关思想，并给出一些相应的研究结果。     

An ontology based approach to organize multi-agent assisted supply chain negotiations

Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. Negotiation is an essential approach to solve conflicting transaction and scheduling problems among supply chain members. The multi-agent system (MAS) technology has provided the potential of automating supply chain negotiations to alleviate human interactions. Software agents are supposed to perform on behalf of their human owners only when equipped with sophisticated negotiation knowledge. To better organize the negotiation knowledge utilized by agents and facilitate agents’ adaptive negotiation decision making ability, an ontology-based approach is proposed in this paper. Firstly, the multi-agent assisted supply chain negotiation scheme is presented to configure the general design components of the negotiation system, covering the agent intelligence modules, the knowledge organization method and the negotiation protocol. Then, the ontology-based negotiation knowledge organization method is specified. The negotiation knowledge is separated into shared negotiation ontology and private negotiation ontology to ensure both the agent communicative interoperability and the privacy of strategic knowledge. Inference rules are defined on top of the private negotiation ontology to guide agents’ reasoning ability. Through this method, agents’ negotiation behaviors will be more adaptive to various negotiation environments utilizing corresponding negotiation knowledge.     

COOPERATIVE LEARNING BY POLICY-SHARING IN MULTIPLE AGENTS

Reinforcement learning is one of the more prominent machine-learning technologies due to its unsupervised learning structure and ability to continually learn, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from the other agents in the system so the speed of learning can be accelerated. In the proposed learning algorithm, an agent adapts to comply with its peers by learning carefully when it obtains a positive reinforcement feedback signal, but should learn more aggressively if a negative reward follows the action just taken. These two properties are applied to develop the proposed cooperative learning method. This research presents the novel use of the fastest policy hill-climbing methods of Win or Lose Fast (WoLF) with policy-sharing. Results from the multi-agent cooperative domain illustrate that the proposed algorithms perform better than Q-learning alone in a piano mover environment. It also demonstrates that agents can learn to accomplish a task together efficiently through repetitive trials.     

Coordinating Agents in Organizations Using Social Commitments

One of the main challenges faced by the multi-agent community is to ensure the coordination of autonomous agents in open heterogeneous multi-agent systems. In order to coordinate their behaviour, the agents should be able to interact with each other. Social commitments have been used in recent years as an answer to the challenges of enabling heterogeneous agents to communicate and interact successfully. However, coordinating agents only by means of interaction models is difficult in open multi-agent systems, where possibly malevolent agents can enter at any time and violate the interaction rules. Agent organizations, institutions and normative systems have been used to control the way agents interact and behave. In this paper we try to bring together the two models of coordinating agents: commitment-based interaction and organizations. To this aim we describe how one can use social commitments to represent the expected behaviour of an agent playing a role in an organization. We thus make a first step towards a unified model of coordination in multi-agent systems: a definition of the expected behaviour of an agent using social commitments in both organizational and non-organizational contexts.     

Enhancing decentralized service discovery in open service-oriented multi-agent systems

Service-oriented multi-agent systems are dynamic systems that are populated by heterogeneous agents. These agents model their functionality as services in order to allow heterogeneous agents or other entities to interact with each other in a standardized way. Furthermore, due to the large-scale and adaptative needs of the system, traditional directory facilitators or middle-agents are not suitable for the management of agent services. This article proposes the introduction of homophily in service-oriented multi-agent systems to create efficient decentralized and self-organized structures where agents have a greater probability of establishing links with similar agents than with dissimilar ones. This similarity is based on two social dimensions: the set of services that an agent provides and the organizational roles that it plays. A second contribution is an algorithm for service discovery that it is carried out taking into account the local information that is related to the homophily between agents. The experiments compare our proposal with other proposals in distributed environments. The results show that the proposed structure and algorithm offer desirable features for service discovery in decentralized environments. Specifically, these features provide short paths and a high success rate in the service discovery process and resilience under deliberate failures.     

一种基于社会承诺的Agent组织模型OMSC

多Agent领域所面临的一个重大的挑战是解决开放异质的多Agent系统中自治Agent间的协调问题。多Agent为了协调它们之间的活动,需要进行交互。社会承诺作为一种通信和交互机制,为自治的多Agent提供了一种协调的途径。然而,仅靠交互难以实现多Agent间的协调。Agent组织作为一种协调模型可以有效地控制多Agent间的交互与合作。论文将社会承诺和Agent组织两种协调机制相结合,提出一种基于社会承诺的Agent组织模型OMSC,分析了Agent如何用社会承诺进行推理以及基于社会承诺的多Agent系统并给出了一个实例,为多Agent间的协调提供了一种新的方法。     

Using Domain-Independent Exception Handling Services to Enable Robust Open Multi-Agent Systems: The Case of Agent Death

This paper addresses a simple but critical question: how can we create robust multi-agent systems out of the often unreliable agents and infrastructures we can expect to find in open systems contexts? We propose an approach to this problem based on distinct exception handling (EH) services that enact coordination protocol-specific but domain-independent strategies to monitor agent systems for problems (‘exceptions’) and intervene when necessary to avoid or resolve them. The value of this approach is demonstrated for the ‘agent death’ exception in the Contract Net protocol; we show through simulation that the EH service approach provides substantially improved performance compared to existing approaches in a way that is appropriate for open multi-agent systems.     

Asymptotic behaviours of a class of threshold models for collective action in social networks

A class of dynamic threshold models is proposed for describing the upset of collective actions in social networks. The agents of the network have to decide whether to undertake certain action or not. They make their decision by comparing the activity level of their neighbours with a time-varying threshold, evolving according to a time-invariant opinion dynamic model. Key features of the model are a parameter representing the degree of self-confidence of the agents and the mechanism adopted by the agents to evaluate the activity level of their neighbours. The case in which a radical agent, initially eager to undertake the action, interacts with a group of ordinary agents, is considered. The main contribution of the paper is the complete characterisation of the asymptotic behaviours of the network, for three different graph topologies. The asymptotic activity patterns are determined as a function of the self-confidence parameter and of the initial threshold of the ordinary agents. Numerical validation on a real ego network shows that the theoretical results obtained for simple graph structures provide useful insights on the network behaviour in more complex settings.     

ResIoT: An IoT Social Framework Resilient to Malicious Activities

The purpose of the next internet of things (IoT) is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting. The convergence of IoT and multi-agent systems (MAS) provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine (M2M) coopera-tion among smart entities. However, the selection of reliable partners for cooperation represents a hard task in a mobile and federated context, especially because the trustworthiness of devices is largely unreferenced. The issues discussed above can be synthesized by recalling the well known concept of social resilience in IoT systems, i.e., the capability of an IoT network to resist to possible attacks by malicious agent that potentially could infect large areas of the network, spamming unreliable infor-mation and/or assuming unfair behaviors. In this sense, social resilience is devoted to face malicious activities of software agents in their social interactions, and do not deal with the correct working of the sensors and other information devices. In this setting, the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities. In this paper, we propose a framework for agents operating in an IoT environment, called ResIoT, where the formation of communities for collaborative purposes is performed on the basis of agent reputation. In order to validate our approach, we performed an experimental campaign by means of a simulated framework, which allowed us to verify that, by our approach, devices have not any economic convenience to performs misleading behaviors. Moreover, further experimental results have shown that our approach is able to detect the nature of the active agents in the systems (i.e., honest and malicious), with an accuracy of not less than 11% compared to the best competitor tested and highlighting a high resilience with respect to some malicious activities.      

Dialogue games that agents play within a society

Human societies have long used the capability of argumentation and dialogue to overcome and resolve conflicts that may arise within their communities. Today, there is an increasing level of interest in the application of such dialogue games within artificial agent societies. In particular, within the field of multi-agent systems, this theory of argumentation and dialogue games has become instrumental in designing rich interaction protocols and in providing agents with a means to manage and resolve conflicts. However, to date, much of the existing literature focuses on formulating theoretically sound and complete models for multi-agent systems. Nonetheless, in so doing, it has tended to overlook the computational implications of applying such models in agent societies, especially ones with complex social structures. Furthermore, the systemic impact of using argumentation in multi-agent societies and its interplay with other forms of social influences (such as those that emanate from the roles and relationships of a society) within such contexts has also received comparatively little attention. To this end, this paper presents a significant step towards bridging these gaps for one of the most important dialogue game types; namely argumentation-based negotiation (ABN). The contributions are three fold. First, we present a both theoretically grounded and computationally tractable ABN framework that allows agents to argue, negotiate, and resolve conflicts relating to their social influences within a multi-agent society. In particular, the model encapsulates four fundamental elements: (i) a scheme that captures the stereotypical pattern of reasoning about rights and obligations in an agent society, (ii) a mechanism to use this scheme to systematically identify social arguments to use in such contexts, (iii) a language and a protocol to govern the agent interactions, and (iv) a set of decision functions to enable agents to participate in such dialogues. Second, we use this framework to devise a series of concrete algorithms that give agents a set of ABN strategies to argue and resolve conflicts in a multi-agent task allocation scenario. In so doing, we exemplify the versatility of our framework and its ability to facilitate complex argumentation dialogues within artificial agent societies. Finally, we carry out a series of experiments to identify how and when argumentation can be useful for agent societies. In particular, our results show: a clear inverse correlation between the benefit of arguing and the resources available within the context; that when agents operate with imperfect knowledge, an arguing approach allows them to perform more effectively than a non-arguing one; that arguing earlier in an ABN interaction presents a more efficient method than arguing later in the interaction; and that allowing agents to negotiate their social influences presents both an effective and an efficient method that enhances their performance within a society.     

Semantic Web service discovery: state-of-the-art and research challenges

Ubiquitous computing paradigms involving social agents require principled selection of services, context-aware analysis, and satisfaction of requests, as well as dynamic interaction and negotiation with other agents. Synergies between semantic technologies and service discovery facilitate rich and formal representations of services and agent interactions as well as specialization and generalization of service needs. In this paper, we provide an extensive review of semantic Web service discovery, highlighting the state-of-the-art approaches, the key semantic formalisms employed, as well as benchmarks and testbeds for performance evaluation. Defining a generic framework for semantic service discovery, we describe the key tasks and criteria involved in agent-based computing. A detailed comparison of the popular discovery systems is performed with a discussion on trade-offs between existing approaches. We conclude by pointing out important research challenges to be addressed for next-generation service discovery by dynamic multi-agent systems in complex environments.