Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.     

Programming Rational Agents in a Modal Action Logic

In this paper we describe a language for reasoning about actions that can be used for modelling and for programming rational agents. We propose a modal approach for reasoning about dynamic domains in a logic programming setting. Agent behavior is specified by means of complex actions which are defined using modal inclusion axioms. The language is able to handle knowledge producing actions as well as actions which remove information. The problem of reasoning about complex actions with incomplete knowledge is tackled and the temporal projection and planning problems is addressed; more specifically, a goal directed proof procedure is defined, which allows agents to reason about complex actions and to generate conditional plans. We give a non-monotonic solution for the frame problem by making use of persistency assumptions in the context of an abductive characterization. The language has been used for implementing an adaptive web-based system.     

Learning market prices in real-time supply chain management

This paper proposes a model for dynamic pricing that combines knowledge of production capacity and existing commitments, reasoning about uncertainty and learning of market conditions in an attempt to optimise expected profits. In particular, the market conditions are represented as a set of probabilities over the success rate of product prices, and those prices are learned online as the market develops. The dynamic pricing model is integrated into a real-time supply chain management agent using the Trading Agent Competition Supply Chain Management game as a test framework. We evaluate the agent experimentally in competition with other supply chain agents, and demonstrate the benefits of incorporating more market data into the dynamic pricing mechanism.     

支持人机协同的智能用户界面体系结构

从分析实现人机协同智能用户界面需解决的问题入手,探讨了多Agent系统解决上述问题的技术途径;提出一种基于多Agent系统的人机协同式智能用户界面体系结构,并重点讨论了如何解决多媒体对象展示关系、Agent之间协同、应用语义反馈、用户智能向导等问题。在该结构中,控制Agent协调可视元素之间的通信,用户Agent负责保存用户兴趣模型和提供推理服务。支持用户界面按构件方式进行组合,具有良好的扩展必和可重用性。模拟实例验证了该体系结构的可行性。     

基于情境演算的智能体结构

Agent结构的建立是Agent研究的重要内容.尝试着结合BDI结构和情境演算的优点,提出了一个能够刻画Agent的多种特征,尤其是自主性的智能体结构AASC(Agent architecture based on situation calculus).此结构既能表示Agent的信念、目标、策略等心智状态,又能进行行动推理和规划,为解释Agent的自主性、建构不同类型的Agent提供了统一的平台.     

一种适合开放环境的Agent体系结构

通过对著名的BDIAgent模型进行改进,并引入元级推理机制而给出了一个适合开放环 境的Agent体系结构。该体系结构具有高效、可扩展的、易于实现等特点。同时,该体系结构还解决 了Agent构造中的一系列难题。     

基于使命的Agent组织模型

Agent组织是一种灵活有效的多Agent系统求解方式。针对目前Agent组织模型主要以利己型Agent为基础的现状,提出适合政府及军事等领域使用的基于使命的Agent组织模型。模型以利他型Agent为组织构成基础,使用信任度和Agent组织效益共同评价Agent求解问题的能力,从Agent思维属性及其相互关系的角度出发,研究了Agent组织中使命的特点、组织形成和演化机制,提高了问题求解效率,增强了Agent组织的稳定性,从而拓宽了关于Agent组织的研究。     

Modeling of process parameter selection with mathematical logic for process planning

Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages. One of the key problems of computer-aided process planning (CAPP), however, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Process knowledge representation and inference mechanism of process parameter selection is one of the most important issues in the research on CAPP. A proper methodology for modeling inference mechanism of process parameter selection, hence, is essential for selection of process parameters in process planning. The paper presents an atomic inference engine model of process parameter selection in process planning using mathematical logic. The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. An illustrative case has been analyzed using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of a hole-making in a industrially relevant workpiece. The outcomes of this work provide a process reasoning mechanism for process parameter selection in process planning and thus alleviate automated process reasoning problems in process planning.     

一个智能用户接口Agent设计与实现

文章主要介绍了DOLTRI-Agent(distance and open learning training resource information retrieval agent)系统中的用户接口Agent(NanDa user interface agent, 简称NDUIA)的设计和实现的关键技术.此系统扩展了memory-based reasoning技术,采用了多个记忆模型和多个分析模型,通过对不同用户使用经验的分析产生该用户专用的用户兴趣模型;同时根据用户兴趣模型和特定场景的使用经验共同作用来提供主动的智能服务,包括信息导引、搜索结果的预处理、智能即时帮助和分类信息的修改等,从而实现软件与人的协作.     

Managing computer files via artificial intelligence approaches

Agent-oriented computing has been regarded as a very promising methodology to developing intelligent software systems. Intelligent agent technology has, thus, been successfully applied in many industrial and commercial areas. Cased based reasoning (CBR) is an effective and efficient analogical reasoning method for solving problems using the knowledge of past experiences, which are stored in a knowledge base as cases. CBR has been extensively employed to tackle such problems as design, planning, classification, and advising in many different application fields. On the other hand, as various files are created on computers, how to efficiently manage computer files becomes a significant issue. So far, there are a number of file management systems available. However, none of them can deal with these crucial problems of file management: Which files should be deleted after their use? Which files should be temporarily kept or permanently preserved? To the best of our knowledge, these problems have not yet been investigated in the open literature. To bridge this gap, in this paper we explore the value of the above artificial intelligence approaches in managing computer files. We develop an intelligent agent based personal file management system, where CBR is employed to guide users to managing their files. Through extensive practical experiments, we validate the effectiveness and efficiency of the developed system.     

Trends in Agent Communication Language

Agent technology is an exciting and important new way to create complex software systems. Agents blend many of the traditional properties of AI programs—knowledge–level reasoning, flexibility, proactiveness, goal–directedness, and so forth—with insights gained from distributed software engineering, machine learning, negotiation and teamwork theory, and the social sciences. An important part of the agent approach is the principle that agents (like humans) can function more effectively in groups that are characterized by cooperation and division of labor. Agent programs are designed to autonomously collaborate with each other in order to satisfy both their internal goals and the shared external demands generated by virtue of their participation in agent societies. This type of collaboration depends on a sophisticated system of inter–agent communication. The assumption that inter–agent communication is best handled through the explicit use of an agent communication language (ACL) underlies each of the articles in this special issue. In this introductory article, we will supply a brief background and introduction to the main topics in agent communication.     

基于协同多Agent的智能化产生式CAPP系统框架

该文提出的协同多Agent产生式系统,将一个复杂的工艺设计问题分解成几个简单的问题,分别采用Agent进行解决,各个Agent具有独自的知识和推理结构,在协同的机理下组合成一个有机的系统。采用多Agent协同结构的智能化产生式CAPP系统能够有效地解决产生式系统的灵活性、智能化、协同化和虚拟化,并能够不断地产生新的知识和方法,并加以保存。该文提出的采用多Agent协同结构的智能化产生式CAPP系统框架及其运行机制,能够在实践中发挥较大的作用,有助于产生新的产生式CAPP系统。     

Introducing a novel model of belief–desire–intention agent for urban land use planning

Land use planning is a potentially demanding search and optimization task that has been challenged by numerous researchers in the field of spatial planning. Agent and multi-agent systems are examples of the modern concepts, which have been gaining more attention in challenging spatial issues recently. Although the efficiency of belief, desire, and intention (BDI) architecture of agents is validated in varieties of sciences, its uses in Geospatial Information Systems (GIS) and specifically among spatial planners is still burgeoning. In this paper, we attempted to integrate the concepts of BDI agent architecture into spatial issues; as a result, a novel spatial agent model is designed and implemented to analyze the urban land use planning. The proposed approach was checked in urban land use planning problems using a case study in a municipal area. The result of implementation showed the effects of spatial agents' behaviors such as intention, commitment, and interaction on their decision.     

软件Agent的解释功能

在一些应用领域中,Agent对其使用者的透明性是相当重要的,因此有必要在Agent结构中加入解释机构增强它的透明性。虽然Agent中加入解释机构将增加Agent的复杂性,但是由于具有解释功能使得Agent的可信赖性增强,并且更能体现Agent的友善性,因此也就更容易被Agent的使用者接受,更有利于Agent的应用与推广。     

多 Agent 系统仿真平台

Agent和多Agent（MAS）系统目前已经逐步走向成熟化,且已经被广泛应用于系统建模、敏捷制造、网络监测等众多领域。为了能够便于Agent系统的开发,人们构建了各种Agent基础设施平台,例如已经广泛应用的JADE等。但是基于这些平台的应用大多具有平台依赖性,难于扩展和定制,无法支撑我们大规模的扩展使用。因此,将通过多Agent系统仿真平台Maze的设计与实现,着力构建出Agent系统各种基础设施的通用开发模式,包括Agent自治机制、通信机制、协作机制、协调机制等,从而便于用户在此基础上进行定制开发。另外,该平台的形象演示可以帮助初学者理解Agent的各种特性（如自治性、社会性、预动性等）,比较各种协作算法的优劣等,是一个很好的入门学习工具。     

From logic programming towards multi‐agent systems

In this paper we present an extension of logic programming (LP) that is suitable not only for the “rational” component of a single agent but also for the “reactive” component and that can encompass multi‐agent systems. We modify an earlier abductive proof procedure and embed it within an agent cycle. The proof procedure incorporates abduction, definitions and integrity constraints within a dynamic environment, where changes can be observed as inputs. The definitions allow rational planning behaviour and the integrity constraints allow reactive, condition‐action type behaviour. The agent cycle provides a resource‐bounded mechanism that allows the agent’s thinking to be interrupted for the agent to record and assimilate observations as input and execute actions as output, before resuming further thinking. We argue that these extensions of LP, accommodating multi‐theories embedded in a shared environment, provide the necessary multi‐agent functionality. We argue also that our work extends Shoham’s Agent0 and the BDI architecture. This revised version was published online in June 2006 with corrections to the Cover Date.     

多级Agent系统安全体系设计与实现

针对多级Agent系统的特点和系统中存在的安全问题,通过制定通信规则、建立自上而下的认证体系、使用加/解密算法等安全技术,设计实现一个多级Agent系统的安全体系。使用由Python语言搭建的系统平台,对安全体系的安全性及时效性进行测试,结果证明了该安全体系的有效性。     

Temporal reasoning for manufacturing: A hybrid representation and complexity management

Temporal considerations play a key role in the planning and operation of a manufacturing system. The development of a temporal reasoning mechanism would facilitate effective and efficient computer-aided process planning and dynamic scheduling. We feel that a temporal system that makes use of the expressive power of the integral language and the computational ease of the point language will be best suited to reasoning about time within the manufacturing system. The concept of a superinterval, or a collection of intervals, is used to augment a hybrid point-interval temporal system. We have implemented a reasoning algorithm that can be used to aid temporal decision making within the manufacturing environment. Using the quantitative results obtained by measuring our program's performance, we show how the superinterval can be used to partition large temporal systems into smaller ones to facilitate distributed processing of the smaller systems. The distributed processing of large temporal systems helps achieve real-time temporal decision-making capabilities. Such a reasoning system will facilitate automation of the planning and scheduling functions within the manufacturing environment and provide the framework for an autonomous production facility.     

A model for reasoning about persistence and causation

Reasoning about change requires predicting how long a proposition, having become true, will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe that a proposition persists indefinitely simply because there is no way for the agent to infer a contravening proposition with certainty. In this paper, we describe a model of causal reasoning that accounts for knowledge concerning cause-and-effect relationships and knowledge concerning the tendency for propositions to persist or not as a function of time passing. Our model has a natural encoding in the form of a network representation for probabilistic models. We consider the computational properties of our model by reviewing recent advances in computing the consequences of models encoded in this network representation. Finally, we discuss how our probabilistic model addresses certain classical problems in temporal reasoning (e. g., the frame and qualification problems).