Improving the Behavior of Intelligent Tutoring Agents with Data Mining

This article presents a novel framework for adapting the behavior of intelligent agents. The framework consists of an extended sequential pattern mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human agents executing a procedural task. The proposed framework has been integrated within the CanadarmTutor, an intelligent tutoring agent aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. We present the results of an evaluation that demonstrates the benefits of this integration to agents acting in ill-defined domains.     

Agent Mining: The Synergy of Agents and Data Mining

Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.     

Ubiquitous Agents for Ambient Ecologies

The cost of state-of-the-art devices and robots is critical for the uptake of Ambient Intelligence (AmI). One way to utilize low-cost hardware for both devices and robots is to run smart software remotely as agents deployed on computationally rich environments. In this context, the devices and the robots can be seen as the Avatars of agents, while the way devices and agents are related may be considered as an Ambient Ecology. We show how the application of a middleware platform called EVATAR realizes this vision by exemplifying the main issues with a multi-robot and a smart-home scenarios.     

基于滑动窗口的支持泛在应用的流聚类挖掘算法

近年来,泛在数据流挖掘逐渐成为数据挖掘发展的新热点,它具有在有限的资源上去挖掘无限的数据流,并可随时随地返回挖掘结果的特点,对此,本文提出一种基于滑动窗口的流聚类算法;该方法将一个滑动窗口分成n个大小相等的窗口单元,基于窗口单元进行增量式的知识相关性的挖掘,提高了流挖掘的效率;当窗口滑动时,通过衰变函数衰减当前滑动窗口内的第一个窗口单元的挖掘结果,并在当前滑动窗口挖掘结果中将其剔除,实现下一滑动窗口的增量式挖掘.     

Shared State for Distributed Interactive Data Mining Applications

Distributed data mining applications involving user interaction are now feasible due to advances in processor speed and network bandwidth. These applications are traditionally implemented using ad-hoc communication protocols, which are often either cumbersome or inefficient. This paper presents and evaluates a system for sharing state among such interactive distributed data mining applications, developed with the goal of providing both ease of programming and efficiency. Our system, called InterAct, supports data sharing efficiently by allowing caching, by communicating only the modified data, and by allowing relaxed coherence requirement specification for reduced communication overhead, as well as placement of data for improved locality, on a per client and per data structure basis. Additionally, our system supports the ability to supply clients with consistent copies of shared data even while the data is being modified.We evaluate the performance of the system on a set of data mining applications that perform queries on data structures that summarize information from the databases of interest. We demonstrate that providing a runtime system such as InterAct results in a 10–30 fold improvement in execution time due to shared data caching, the applications' ability to tolerate stale data (client-controlled coherence), and the ability to off-load some of the computation from the server to the client. Performance is improved without requiring complex communication protocols to be built into the application, since the runtime system uses knowledge about application behavior (encoded by specifying coherence requirements) in order to automatically optimize the resources utilized for communication. We also demonstrate that for our benchmark tests, the quality of the results generated is not significantly deteriorated due to the use of more relaxed coherence protocols.     

Guest Editors' Introduction: Agents and Data Mining

On top of two active research streams, agents and data mining, a most recent and exciting trend is their interaction and integration. Agent mining has emerged as a very promising field due to its unique contributions to complementary and innovative methodologies, techniques, and applications for complex problem-solving. This editorial summarizes the structure of this special issue.     

可视化驱动的交互式数据挖掘方法综述

数据挖掘是一种从大量数据中发现信息的过程,其大量依赖自动算法的特质,使得用户难以对数据和算法过程本身直观地进行理解、探索和优化.近年来,随着可视化领域的蓬勃发展,有很多工作开始探究如何使用可视化方法辅助数据挖掘过程,使用户更加直观地理解数据,并对数据和算法和进行探索.文中首先对数据挖掘和可视化在知识提取流程进行比较分析,并从可视化增强的通用数据挖掘方法和面向应用场景的方法 2个方面对近年相关技术进行梳理总结,并依托一些相关主题的国际会议内容指出需要进一步探索的方向.     

VISMiner：一个交互式可视化数据挖掘原型系统

交互式可视化数据挖掘是利用可视化技术进行联机数据挖掘的技术。基于SOM的交互式可视化数据挖掘原型系统VISMiner的主要目的是将数据挖掘与数据可视化及OLAP进行集成,允许用户以交互的方式从SOM的标记图或距离图中选定感兴趣区域加以深入分析。     

一种大数据交互式挖掘框架与实现

在传统的数据挖掘过程中,用户需根据专业知识对数据进行预处理,为模型设定参数后构建模型,通过评估指标判断模型是否可行。该过程的不便性体现在模型以黑盒的方式构建,用户不可见其中间过程,模型产生的结果也不易被理解。在海量数据的环境下,传统数据挖掘过程在预处理时异常数据的定位和模型生成后知识的表达方面都有不便。为了解决传统数据挖掘过程存在的问题,本文提出了一种大数据环境下的交互式数据挖掘框架。该框架使交互贯穿整个数据挖掘的过程,使得用户可以轻松定位异常输入源数据,参与模型训练过程,对模型生成的结果溯源。本文还基于Spark对该框架进行了实现,并在食源性疾病爆发预测场景下验证了其可行性。     

基于中医疗效评价的交互式数据挖掘框架

设计并实现了基于小儿肺炎中医疗效评价的交互式数据挖掘框架。该框架采用数据挖掘、数理统计和逻辑分析相结合的方法,通过回顾性和前瞻性多角度的验证与比较研究,揭示各证和所属症状之间的关联性,不仅突破了传统的疗效评价方法的限制,而且优化了疗效规范,建立了客观的、人机交互可度量的病证结合的疗效评价体系。     

基于数据挖掘、智能代理的入侵检测和响应

入侵检测和响应问题一直是诸多计算机科学家们努力去解决而又至今尚未完全解决的一个问题，该文引入数据挖掘技术和智能代理技术，提出了由传感器、检测器、数据仓库、模型生成器及分析机制等组成的体系机构。该机构能很方便地对大量特征数据进行存储、共享，使检测模型的生成、分发自动化，使大量异构数据的处理智能化，提高了入侵检测和响应的精确性、效率和速度。     

基于数据抽取器实现数据挖掘

通过定义ＳＯＬ数据挖掘抽取器,设计了数据挖掘算法和数据库管理系统的接口的框架体系。并通过一个常用的数据挖掘算法简单贝叶斯算法说明了这种标准的ＳＯＬ数据挖掘抽取器的适用性。     

基于虚拟现实的互动普适计算研究

普适计算与虚拟现实技术的融合是今天普适计算的一个新领域,是对虚拟空间计算力的提升,更是普适计算中"普适"的体现。本文讨论并给出了基于虚拟现实技术的互动普适计算的概念、特性和关键技术,为其实现提供理论探索和论证。     

A Formal Framework for Interactive Agents

This paper proposes a formal framework and architecture for specification and analysis of interactive agents. The framework can be used to explore the design space, study features of different points in the design space, and to develop executable specifications of specific agents and study their interactions with the environment. A long term goal is development of reasoning principles specialized to different regions of the design space.     

交互式智能体体系结构应用研究

介绍和分析了目前关于智能体体系结构的研究,重点以无人作战武器防御系统为背景,深入研究交互式智能体体系结构的应用,并对智能体的协调机制、通信机制、不完全全局规划方法等作了较深入的阐述。     

Data Mining with Sparse Grids

O (h _n ⁻¹ n ^{d −1}) instead of O(h _n ^−d ) grid points and unknowns are involved. Here d denotes the dimension of the feature space and h _n = 2 ⁻ⁿ gives the mesh size. To be precise, we suggest to use the sparse grid combination technique [42] where the classification problem is discretized and solved on a certain sequence of conventional grids with uniform mesh sizes in each coordinate direction. The sparse grid solution is then obtained from the solutions on these different grids by linear combination. In contrast to other sparse grid techniques, the combination method is simpler to use and can be parallelized in a natural and straightforward way. We describe the sparse grid combination technique for the classification problem in terms of the regularization network approach. We then give implementational details and discuss the complexity of the algorithm. It turns out that the method scales only linearly with the number of instances, i.e. the amount of data to be classified. Finally we report on the quality of the classifier built by our new method. Here we consider standard test problems from the UCI repository and problems with huge synthetical data sets in up to 9 dimensions. It turns out that our new method achieves correctness rates which are competitive to that of the best existing methods. Received April 25, 2001     

一种交互式的约束关联规则开采算法

该文针对传统关联规则开采算法的不足,提出了一种交互式的基于约束的关联规则开采算法,解决了特定应用下的数据采掘及其效率问题。     

基于约束的交互式频繁模式挖掘算法

频繁模式挖掘是数据挖掘领域中很重要的一部分.目前,出现了许多基于约束的频繁模式挖掘算法和交互式算法,但把两者结合起来的算法却很少.提出了一种基于约束的交互式频繁模式挖掘算法IMCFP(interactive mining of constraint-based frequent patterns).首先该算法按照约束的性质来建立频繁模式树,并且只需扫描一遍数据库;然后建立每个项的条件树,挖掘以该项为前缀的最大频繁模式,并用最大频繁模式树来存储;最后根据最大模式来找出所有的支持度明确的频繁模式.另外,该算法允许用户在挖掘过程中动态地改变约束.实验表明,该算法与iCFP算法相比是很有效的.     

Interactive Navigation of Heterogeneous Agents Using Adaptive Roadmaps

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive roadmaps to perform global path planning for each agent simultaneously. Our algorithm takes into account dynamic obstacles and interagents interaction forces to continuously update the roadmap based on a physically-based dynamics simulator. In order to efficiently update the links, we perform adaptive particle-based sampling along the links. We also introduce the notion of 'link bands' to resolve collisions among multiple agents. In practice, our algorithm can perform real-time navigation of hundreds and thousands of human agents in indoor and outdoor scenes.     

数据挖掘技术在构造范例库中的应用

将基于范例的推理应用于实际系统中，一个很关键的问题是如何构造一个丰富而高效的范例库。采用了数据挖掘技术， 提出了一个综合算法，从传统数据库中构造范例库，以提高知识获的自动化程度。