A predictive approach for the generation of rhetorical devices

Human discourse is fraught with rhetorical devices such as contradictions, illustrations, and analogies. These rhetorical devices carry important information which a listener uses to speed up the comprehension process. In this paper, we present a taxonomy of rhetorical devices commonly used in tutoring environment, and model the meaning of a class of rhetorical devices in terms of their anticipated effect on a listener's knowledge. These predictions are then used in planning computer-generated discourse. As a testbed for our ideas, a system called WISHFUL is being implemented to generate commentaries in the domain of high-school algebra within the framework of an intelligent tutoring system.     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

A distributed problem-solving approach to collaborative facility engineering

A framework for collaborative facility engineering is presented. The framework is based on a distributed problem-solving approach to collaborative facility engineering and employs an integration approach called Agent-Based Software Engineering as an implementation vehicle of this approach. The focal entity of this framework is a Multiagent Design Team (MDT) that comprises a collection of software agents (e.g. design software applications with a certain standard communication interface) and a design specialist, which together perform specific design tasks. Multiagent design teams are autonomous and form an organizational structure based on a federation architecture. Every multiagent design team surrenders its autonomy to a system program called facilitator, which coordinates the interaction among software agents in the federation architecture. Facilitators can be viewed as representatives of one or more teams that facilitate the exchange of design information and knowledge in support of the design tasks they perform. In the federation architecture, design specialists collaborate by exchanging design information with others via their software agents, and by identifying and resolving design conflicts by negotiation. In addition to a discussion of the framework's primary components, its realization in an integrated distributed environment for collaborative building engineering is described.     

DART: A DISTRIBUTED ANALYSIS OF REPUTATION AND TRUST FRAMEWORK

Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e‐commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation‐resistant, the researchers require a flexible, easy‐to‐use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game‐theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well‐known trust models against both individual and group attacks.     

A conversational intelligent tutoring system to automatically predict learning styles

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61–100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.     

基于知识支持的建筑施工质量控制系统研究

将知识管理的理念应用到施工质量控制中,加速施工质量控制中知识的循环过程,提高工程质量预测控制和事故诊断能力。通过湖北省建筑工程质量控制研究实践,开发了集建筑质量控制知识管理和决策支持功能于一体的施工质量控制和事故处理系统(CQIS),系统将正向规则推理和基于案例的推理(CBR)相结合,通过RBR接口调用RBR系统,提高案例推理质量诊断的能力,实现基于知识的综合推理功能。该系统已经获得科技成果鉴定和湖北省科技推广成果。     

基于宠物游戏的智能教学系统

通过利用游戏宠物扮演各种教育角色,扩展智能教学系统(intelligent tutoring system,ITS)中的学生模型,从而完成宠物游戏与ITS的有机结合.设计了一个基于宠物游戏的游戏化智能教学系统架构,架构中的宠物模块能记录学习者的学习活动、分析学习者的学习进程.基于该软件架构,实现了一款游戏化智能教学系统--宠物星际旅行,将宠物游戏的游戏化特性引入到了小学分数知识的学习过程中.测试结果表明,学生对该游戏越专注,就能越快地理解和运用所学知识.     

TEx-Sys model for building intelligent tutoring systems

Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student’s characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the first time, our approach to research, development and implementation related to intelligent tutoring systems and ITS authoring shells. Our research relies on the traditional intelligent tutoring system, the consideration that teaching is control of learning and principles of good human tutoring in order to develop the Tutor–Expert System model for building intelligent tutoring systems in freely chosen domain knowledge. In this way we can wrap up an ongoing process that has lasted for the previous fifteen years. Prototype tests with the implemented systems have been carried out with students from a primary education to an academic level. Results of those tests are advantageous, according to surveys, and the implemented and deployed software satisfies functionalities and actors’ demands.     

COMET: A Collaborative Tutoring System for Medical Problem-Based Learning

This paper discussed about the developed collaborative intelligent tutoring system for medical PBL called Comet (collaborative medical tutor). Comet uses Bayesian networks to model the knowledge and activity of individual students as well as small groups. It applies generic tutoring algorithms to these models and generates tutorial hints that guide problem solving. An early laboratory study shows a high degree of agreement between the hints generated by Comet and those of experienced human tutors. Evaluations of Comet's clinical-reasoning model and the group reasoning path provide encouraging support for the general framework.     

A customer service process innovation using the integration of data base and case base

Customer service is one of the critical processes that bring competitive advantage in today's business environment. By analyzing the current flow of the customer service process, we identify that lack of datalinformation sharing is the main problem. Therefore, all data and knowledge related to the customer service process need to be accumulated and shared. In this research, we develop an enabler for customer service process innovation by integrating data base technology and case-based reasoning technology. In particular, a case-based system for equipment malfunction diagnosis, which we developed in this research, is described and evaluated in detail.     

A general framework of a Reference Model for Intelligent Integrated Manufacturing Systems (REMIMS)

Today's highly competitive business environment forces the managers to continuously make the best decisions in the shortest possible time. The ability to provide concurrency among manufacturing functions is a critical need for modern organizations as, especially, distributed environment requires synchronization of manufacturing functions. Moreover, manufacturing companies need to have strong capability of adaptation (agility) mainly because of the dynamic relationships that must be established between manufacturing units. To achieve these, there is a need for an integrated manufacturing system that will handle all interactions and interrelationships which will then be affected by the changes and create maximum gain under limited resources. In order to create and effectively manage such an integrated manufacturing system there is a need for a reference model. In this paper, such a reference model called REference Model for intelligent Integrated Manufacturing System (REMIMS) is introduced. REMIMS has hierarchical architecture with several agents responsible for different manufacturing functions. To facilitate REMIMS and allow interaction among the agents to share their knowledge, a specific knowledge exchange protocol in a knowledge network is being developed.     

TRAMMAS: A tracing model for multiagent systems

Agent's flexibility and autonomy, as well as their capacity to coordinate and cooperate, are some of the features which make multiagent systems useful to work in dynamic and distributed environments. These key features are directly related to the way in which agents communicate and perceive each other, as well as their environment and surrounding conditions. Traditionally, this has been accomplished by means of message exchange or by using blackboard systems. These traditional methods have the advantages of being easy to implement and well supported by multiagent platforms; however, their main disadvantage is that the amount of social knowledge in the system directly depends on every agent actively informing of what it is doing, thinking, perceiving, etc. There are domains, for example those where social knowledge depends on highly distributed pieces of data provided by many different agents, in which such traditional methods can produce a great deal of overhead, hence reducing the scalability, efficiency and flexibility of the multiagent system. This work proposes the use of event tracing in multiagent systems, as an indirect interaction and coordination mechanism to improve the amount and quality of the information that agents can perceive from both their physical and social environment, in order to fulfill their goals more efficiently. In order to do so, this work presents an abstract model of a tracing system and an architectural design of such model, which can be incorporated to a typical multiagent platform.     

智能辅助教学写作环境CourseTalk的设计与实现

本文介绍了一个智能辅助教学写作环境CourseTalk的设计与实现.在这个环境中使用了综合媒体结构(Hypermedia)、Prolog和黑板结构等技术,以及推理与查询相结合的搜索策略,并提出一种通过编辑直接定义关系数据以及可以用Prolog语言方便地描述教学策略的方法.同时还讨论了有关知识获取、教学策略选择和学生模型等问题.     

REACTIVE AGENT DESIGN FOR INTELLIGENT TUTORING SYSTEMS

This paper presents a new approach to the analysis and design of intelligent tutoring systems (ITS), based on reactive principles and cognitive models, this way leading to multiagent architecture. In these kinds of models, the analysis problem is treated bottom-up, as opposed to that of traditional artificial intelligence (AI), i.e., top down. We present one ITS example called Makatsina (meaning tutor in TOTONACA, a Mexican pre-Columbian language), constructed according to this approach, which teaches the skills necessary to solve the truss analysis problem by the method of joints. This learning domain is an integration skill. The classical ITS work is based on explicit goals and an internal representation of the environment. The new approach has reactive agents which have no representation of their environment and act using a stimulus response behavior type. In this way they can respond to the present state of the environment in which they are embedded. With these elements, errors, and teaching plans, each agent behaves as an expert assistant that is able to handle different teaching methods. Reactive agent programming is found to be simple because agents have simple behaviors. The difficulty lies in the interaction mechanism analysis and design between the environment and the intelligent reactive system.     

Incorporating tutoring principles into interactive knowledge acquisition

This paper argues that interactive knowledge acquisition systems would benefit from a tighter and more thorough incorporation of tutoring and learning principles. Current acquisition systems learn from users in a passive manner, and could instead be designed to incorporate the proactive capabilities that one expects of a good student. We first describe our analysis of the literature on teacher–student interaction and present a compilation of tutoring and learning principles that are relevant to interactive knowledge acquisition systems. We then point out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly, and discuss how a more thorough and explicit representation of these principles would help improve how computers learn from users. We present our design and an initial implementation of an acquisition dialogue system called SLICK that represents acquisition principles and goals explicitly and declaratively, making the system actively reason about various acquisition tasks and generate its interactions dynamically. Finally, we discuss promising directions in designing acquisition systems by structuring interactions with users according to tutoring and learning principles.     

一个移动Agent算法性能评价的仿真环境

移动Agent是一种新的分布计算技术，自90年代被提出以来受到广泛关注。研究人员提出了很多基于移动Agent的算法，以解决传统分布式计算问题和移动计算中的问题。然而由于移动Agent所具有的反应性、自治性、可移动性等的特性使得对移动Agent的算法的性能评价成为一个相当复杂的问题。本文使用直接执行仿真(Direct Execution Simulation)的方法建立了一个移动Agent系统的仿真环境MADESE，通过执行移动Agent代码来获取其基本数据，并通过仿真计算出移动Agent算法在各种环境中的性能。文章讨论了MADESE的事件和同步机制以及其系统环境的设计结构，并介绍了其实现原型。     

Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing

Designers face many system optimization problems when building distributed systems. Traditionally, designers have relied on optimization techniques that require either prior knowledge or centrally managed runtime knowledge of the system's environment, but such techniques are not viable in dynamic networks where topology, resource, and node availability are subject to frequent and unpredictable change. To address this problem, we propose collaborative reinforcement learning (CRL) as a technique that enables groups of reinforcement learning agents to solve system optimization problems online in dynamic, decentralized networks. We evaluate an implementation of CRL in a routing protocol for mobile ad hoc networks, called SAMPLE. Simulation results show how feedback in the selection of links by routing agents enables SAMPLE to adapt and optimize its routing behavior to varying network conditions and properties, resulting in optimization of network throughput. In the experiments, SAMPLE displays emergent properties such as traffic flows that exploit stable routes and reroute around areas of wireless interference or congestion. SAMPLE is an example of a complex adaptive distributed system.     

DESIGNING COMPLEX SYSTEMS WITHIN DISTRIBUTED ARCHITECTURES: AN INTELLIGENT TUTORING SYSTEMS PERSPECTIVE

The goal of this work is to introduce the basic concepts of distributed artificial intelligence as new methodologies to conceive and realize complex intelligent systems. We focused our attention on the design of intelligent tutoring systems, and using a distributed framework, we reached a fine control on the communicability process, one of the key issues in the realization of intelligent tutoring systems. A prototype is presented as an open information system in which classical modules, such as the domain expertise module and the tutor, are seen as populations of intelligent actors that share their fragmentary knowledge and organize their tasks in a concurrent universe in order to accomplish a common goal, the teaching session. The system has been written in a concurrent object-oriented language, realized on top of the Common Lisp Object System.     

An abstract knowledge based approach to diagnosis and recovery of plan failure in multi-agent systems

Open multi-agent systems (MAS) are decentralised and distributed systems that consist of a large number of loosely coupled autonomous agents. In the absence of centralised control they tend to be difficult to manage, especially in an open environment, which is dynamic, complex, distributed and unpredictable. This dynamism and uncertainty in an open environment gives rise to unexpected plan failures. In this paper we present an abstract knowledge based approach for the diagnosis and recovery of plan action failures. Our approach associates a sentinel agent with each problem solving agent in order to monitor the problem solving agent’s interactions. The proposed approach also requires the problem solving agents to be able to report on the status of a plan’s actions.Once an exception is detected the sentinel agents start an investigation of the suspected agents. The sentinel agents collect information about the status of failed plan abstract actions and knowledge about agents’ mental attitudes regarding any failed plan. The sentinel agent then uses this abstract knowledge and the agents’ mental attitudes, to diagnose the underlying cause of the plan failure. The sentinel agent may ask the problem solving agent to retry their failed plan based on the diagnostic result.