STORY GENERATION BASED ON DYNAMICS OF THE MIND

A story generation system based on dynamics of the mind is presented. Semantic theories until now avoided discussing a linkage of language with encyclopedic human knowledge. This paper attacks the problem of how to make clear the complicated, wide-ranging dynamics of the mind and how to connect it to language.
First, the background of this study is shown, in which our model of the mind, with both five levels along the concept formation process and eight domains according to the contents of mental activities, is surveyed.
Based on this model, then, the dynamics of mind are discussed, along an Aesop fable. A unit of data processing in the mind is called a "module," and the mental dynamics are considered as a chain activation of those modules, represented by a dynamic network. Next, a method for story generation is discussed. Propositional information of a sentence is embedded in a slot of activated, frame-structured modules, and the discourse structure of a generated story basically depends on the dynamic network. Both the chain activation and the generation processes are verified by experiments.
Lastly, residual problems of our research, a comparison with related research, and its applications are discussed.
This case study would be expected to give a basis to integrated systems for problem solving, natural language and image understandings, their generations, and intelligent robots.     

一种结合环境状态的Agent语义模型

在Agent模型的研究中，大部分工作集中在基于Agent的思维状态（BDI）的模型，没有考虑外部环境的影响，或者仅仅将外部环境和内部思维状态笼统混在一起进行分析，没有考虑到它们之间的内在联系，将Agent的BDI思维属性模型与外部环境状态相结合，给出了在部分可观察环境下，结合外部环境状态的MAS语言语法，语义模型，考虑了Agent的可见算子，观察算子和信念算子之间的关系，并通过机器人足球赛的例子，对该语义系统加以具体描述，这些研究推广了Kaelbling,Wooldridge等人的工作。     

多Agent环境下规划的验证方法

如何验证多ａｇｅｎｔ规划是一个非常重要的研究课题，因为该研究关系到规划的求解能否在分布式环境下有效地进行。在本文中，我们讨论了规划的验证方法，给出了压缩集合的生成算法，并给出了该算法的正确性证明，与Ｋａｔｚ等人所提出的算法不同，我们用正向邻接表和逆向邻接表来表示规划链关系Ｄ，用辅助数组来控制所用到的子图（即不是真正的生成子图，但由辅助数组即能看出当前所要处理的子图），提出了避免无用子图产生的方法。     

STRUCTURING AND MODELING DATA FOR REPRESENTING THE BEHAVIOR OF AGENTS IN THE GOVERNANCE OF THE BRAZILIAN FINANCIAL SYSTEM

This article proposes a method to collect and structure data in order to model the behavior of agents in an agent-based simulation model. This model aims to study the regulatory governance of the Brazilian financial system. In this article, the regulatory governance is understood as a phenomenon that results from several interactions and transactions among all actors that influence or are influenced by the activities of the regulation policies. The study focuses on the short-term interest rates and incorporates behavioral aspects and no-explicit interests of social and economical agents. The developed method integrates content analysis research and in-depth interviews to model the agent' behavior by means of fuzzy logic rules. It provides systematic collection and interpretation of data produced in textual form as well as knowledge from experts. The results of the model validation have shown that this approach contributes to the development of a methodology for the modeling and representation of agent behavior in social applied simulation models.     

USING ABDUCTIVE LOGIC AGENTS FOR MODELING THE JUDICIAL EVALUATION OF CRIMINAL EVIDENCE

In this paper, we present a new approach to the modeling of the judicial evaluation of criminal evidence, an approach based on an abductive multi-agent system. Legal justification in such a context has, as its main objective, the finding of relatively most plausible explanations for given pieces of evidence, especially (but not necessarily) in a criminal trial. In our approach, the process of justification is carried out through the collaborative abductive reasoning of agents, operating within a logic-based architecture called ALIAS. This enables us the modular composition of the knowledge of the different dramatis personæ involved in the trial: the detective, witnesses, suspects, judges, and so forth. Having represented each actor in the trial by a single ALIAS agent, we are able to dynamically group and coordinate them for the explanation of goals (such as, for instance, pieces of evidence or any given observation).

We tested our proposed approach on the Peyer case, which was tried in California. It is an example borrowed from the literature that has been adopted as a testbed by previous abduction-based approaches. We will show that the use of ALIAS agents in legal justification allows us not only to produce plausible explanations for observed pieces of evidence, but also to detect collusions or inconsistencies among trial characters. Moreover, we will show how legal justification with the proposed approach could also take into consideration the credibility of the persons (e.g., witnesses) involved in the trial.     

一种新的软件Agent设计的评价标准:用户自治性

本文提出了软件Ａｇｅｎｔ设计中的用户自治性标准,我们认为用户自治性是Ａｇｅｎｔ设计中需要考虑的重要方面,探讨了自治性标准在Ａｇｅｎｔ设计中的定义以及Ａｇｅｎｔ如何影响用户自治性,并在此基础上提出了一个新的软件系统模型。     

COORDINATION OF DISTRIBUTED ENERGY RESOURCE AGENTS

This article describes our research in technologies for the management and control of distributed energy resources. An agent-based management and control system is being developed to enable large-scale deployment of distributed energy resources. Local intelligent agents will allow consumers who are connected at low levels in the distribution network to manage their energy requirements and participate in coordination responses to network stimuli. Such responses can be used to reduce the volatility of wholesale electricity prices and assist constrained networks during summer and winter demand peaks. In our system, the coordination of energy resources is decentralized. Energy resources coordinate each other to realize efficient autonomous matching of supply and demand in large power distribution networks. The information exchange is through indirect (or stigmergic) communications between agents. The coordination mechanism is asynchronous and adapts to change in an unsupervised manner, making it intrinsically scalable and robust.     

ON THE ROLE OF BDI MODELING FOR INTEGRATED CONTROL AND COORDINATED BEHAVIOR IN AUTONOMOUS AGENTS

This paper describes an architecture for controlling and coordinating autonomous agents, building on previous work addressing reactive and deliberative control methods. The proposed multilayered hybrid architecture allows a rationally bounded, goal-directed agent to reason predictively about potential conflicts by constructing knowledge level models that explain other agents' observed behaviors and hypothesize their beliefs, desires, and intentions; at the same time, it enables the agent to operate autonomously, to react promptly to changes in its real-time environment, and to coordinate its actions effectively with other agents. A principal aim of this research is to understand the role dzfferent functional capabilities play in constraining an agent5 behavior under varying environmental conditions. To this end, an experimental test bed has been constructed comprising a simulated multi-agent world in which a variety of agent configurations and behaviors have been investigated. A number of experimentalfindings are reported.     

THEORY OF QUANTIZED SYSTEMS: DEVS SIMULATION OF PERCEIVING AGENTS

Perceiving agents have a limited sensitivity to distinguish input stimuli. Whatever the cause random noise, limited numbers of sensing elements, intentional desensitization formulation of such behavioral phenomena at the system level has not attracted much interest. This work is a first attempt to formalize the limited-sensitivity abstraction and to explore its implications for systems modeling and simulation, both conceptual and practical. In this article, a system theoretic formalization of perceiving agents called quantized systems is provided, linking them with discrete event modeling paradigms and exploring their approximation capability and message reduction in distributed simulation.     

基于CORBA的分布式Agent通信构架

随着Internet的不断发展,传统的搜索引擎在信息获取上显露出明显的不足。Agent技术的出现使得个性化的主动信息服务成为可能,但是单个Agent的能力有限,因此需要在Agent之间进行通信与交流,实现社会过滤以提高信息服务的质量,为了解决Agent的运行平台和底层通信机制的异构性问题,提出了基于公用对象请求代理体系结构CORBA的分布式Agent通信构架－－CADA.Agent获取用户的信息需求并用KQML进行描述,它通过通信器与其他Agent进行信息交流,通信器对接收到的服务请求进行过滤,将其转发到能够提供该服务的Agent,最后把处理结果返回给客户端．CADA屏蔽了运行平台和底层通信机制,能够有效地解决分布式Agent的通信问题。     

基于软件Agent的电子政务安全设计

同传统的协作式工作平台或工作流系统相比,电子政务对安全性具有更高的要求。根据我国当前电子政务建设中存在的各种安全问题,着重介绍了软件Agent技术,并分别讨论了如何在电子政务的公众网、外网、内网和公文流转的安全设计中利用软件Agent技术来提高系统的安全性。     

理性Agent的意图维护模型

在理性Agent的形式化中,通常认为Agent的思维状态包含信念、愿望帮意图这3个属性,它们之间的静态关系已被人们充分重视,但它们随时间的动态变化还未被很好研究,考察了意图维护直观语义中的一些问题,给出了随信念、愿望改变的意图维护的动态模型,与Rao和Georgeff的意图维护模型相比,适用性更广、更简单,不必限制可友爱世界的时序结构,不必另外引入信念=愿望、意图算子的on ly模态形式,并且对愿望和意图不存在逻辑全知问题和重言隐含下的副作用等问题。     

TAKING AGENTS SERIOUSLY

Stit theory (a logic of seeing-to-it-that) is applied to legal matters. First the background to and nature of stit are set out, including a discussion of agentive sentences, branching time semantics, and the semantics specific to stit. In the second part are calculations involving stit. These stit calculations involve conjunctive actions, conditional actions, refraining, and when an agent could have done otherwise. The third part sets out some stit developments, including accounts of obligation and prohibition, multiple agency, and strategies and promises. Legal cases and actions involving evidence are discussed throughout.     

自由之我

序·自由心生 《surface表面》创刊一周年了。在这一年中我们越来越深刻地发现，打开思想确实能让生活变得更快乐、更富激情。有一群先行者正在感受这样的生活，于是我们努力将这样的经验与更多人分享。我们相信，不论是一件富于想象力的物品，还是一个精彩的人，都有可能成为他人的灵感和动力，激发出他们下一个有趣的尝试，     

一个基于群签名的移动代理安全路由协议

移动代理系统安全一直是个备受关注的问题,尤其是对恶意主机对移动代理(以下简称MA)的攻击的研究目前还处于起步阶段.文献[1]所提出的一种MA安全路由协议的不足并对其改进,其中最重要的一项改进就是将群签名的概念首次引入到MA系统中.改进后的安全路由协议降低了恶意主机集群故意跳过某台应该访问的主机的可能性;免除对不在线或拒绝提供服务的主机的多次徒劳连接;减少每台主机所需持有的其它主机公钥数目和花费在验证MA合法性上的时间;提高MA所有者在追踪过程中查找DoS攻击者的效率.     

COORDINATION DIAGNOSTIC ALGORITHMS FOR TEAMS OF SITUATED AGENTS: SCALING UP

Agents in a team should be in agreement. Unfortunately, they may come to disagree due to sensor uncertainty, intermittent communication failures, etc. Once a disagreement occurs, the agents should detect and diagnose the disagreement. Current diagnostic techniques do not scale well with the number of agents, as they have high communication and computation complexity. We present novel techniques that enable scalability in three ways. First, we use communications early in the diagnostic process to stave off unneeded reasoning, which ultimately leads to unneeded communications. Second, we use light‐weight (and inaccurate) behavior recognition to focus the diagnostic reasoning on beliefs of agents that might be in conflict. Finally, we propose diagnosing only to a limited number of representative agents (instead of all the agents). We examine these techniques in large‐scale teams of situated agents in two domains and show that combining the techniques produces a diagnostic process that is highly scalable in both communication and computation.     

EVA: AN EVOLUTIONARY APPROACH TO MUTUAL MONITORING OF LEARNING INFORMATION AGENTS

A learning agent system is composed of agents able to autonomously enrich their knowledge and improve their performance, using learning strategies. The idea underlying this article is that individual improvements obtained by the learning capabilities of an agent should be exploited to advantage the other agents, and a natural way of obtaining such a result is represented by evolutionary processes. However, the biological evolutionary mechanisms are often too complex to be reproduced in a software environment. In this context, we argue that cloning, due to its very simple mechanism of reproduction, can be usefully used. In our approach, a user in a virtual community can substitute an unsatisfactory agent cloning an existing agent having both similar interests and a good reputation in the community. This mechanism induces an evolutionary process in the community, such that the less satisfactory agents are replaced by more effective agents. The key issue of this proposal is that of suitably selecting the agent to be cloned in the presence of a user's request, and to this purpose we propose an evolutionary model of reputation. Our evolutionary approach has been implemented on top of a leaning agent-based recommender system, and a number of experiments show that this novel strategy introduces significant improvements in the effectiveness of the recommendations.     

SELF-LEARNING AGENTS: A CONNECTIONIST THEORY OF EMOTION BASED ON CROSSBAR VALUE JUDGMENT

This article presents a computational theory of emotions, based on the theory of emotion as value judgment and appraisal. It assumes that what we feel about other people, events, and things, generally indicates how we evaluate them. The basic assumption is that emotion is an internal self-evaluation of something relevant for the existence of the agent, like self-evaluation of the global state the agent is in, and the behavior the agent is about to perform. This work presents an agent architecture which contains the three components of the control system in biological systems?the genetic, neural, and hormonal component. As distillate of the theory, a working architecture that implements value judgment is presented. The architecture is based on a crossbar connectionist adaptive array, which is designed in a way that it computes from the same crossbar memory elements, both emotions toward encountered situations and emotions toward action tendencies. In such a way it actually builds in hardware, inseparable connections between emotions and behavior. This article gives an instantiation of the architecture and describes a learning experiment to illustrate the emotion learning. A discussion that relates this work to other work reinforcement learning and current research in emotion learning agents is also provided.     

URGING DESIRE: MOTIVATIONAL MECHANISMS FOR INTELLIGENT AGENTS WITH MINDS OF THEIR OWN

In this article, we examine ways to organize an intelligent autonomous agent's goal system to simulate the behavior of a human worker in an enterprise modelling system called goal orientated multi-agent simulation environment (GOMASE). In GOMASE, an agent is supplied with a goal hierarchy, activity digraph, and a motivational mechanism in an attempt to simulate the goal's activation and attempted satisfaction. The attractiveness of this approach is that all the goals possessed by the agent are related via the ultimate life goals (i.e., self-preservation and species preservation). By using GOMASE to model enterprises, the enterprise modeller can gain an insight into the complex behavior of human workers and how they set, prioritize, and execute their goals. In this article, we also provide several sample runs of our system, with a brief explanation on the agent's behaviors.