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.     

Boolean factors as a means of clustering of interestingness measures of association rules

Measures of interestingness play a crucial role in association rule mining. An important methodological problem, on which several papers appeared in the literature, is to provide a reasonable classification of the measures. In this paper, we explore Boolean factor analysis, which uses formal concepts corresponding to classes of measures as factors, for the purpose of clustering of the measures. Unlike the existing studies, our method reveals overlapping clusters of interestingness measures. We argue that the overlap between clusters is a desired feature of natural groupings of measures and that because formal concepts are used as factors in Boolean factor analysis, the resulting clusters have a clear meaning and are easy to interpret. We conduct three case studies on clustering of measures, provide interpretations of the resulting clusters and compare the results to those of the previous approaches reported in the literature.     

PVD薄膜生长过程的计算机模拟

运用KMC方法对金属Ti膜的生长过程进行了计算机模拟,在合理选用势函数和相关参数的基础上,全面考虑了表面上所有不稳定团簇粒子发生扩散迁移运动的可能性。在对粒子团簇进行统计分析时,提出根据粒子的不同颜色对粒子团簇进行统计的新方法。模拟结果表明,随基底温度的升高,团簇总数呈现下降趋势,最大团簇尺寸在温度高于350K时,有明显的长大。而沉积速率的变化对粒子团簇的分布和团簇尺寸的影响不大。     

The use of microwave irradiation for the easy synthesis of graphene-supported transition metal nanoparticles in ionic liquids

Stable ruthenium or rhodium metal nanoparticles were supported on chemically derived graphene (CDG) surfaces with small and uniform particle sizes (Ru 2.2 ± 0.4 nm and Rh 2.8 ± 0.5 nm) by decomposition of their metal carbonyl precursors by rapid microwave irradiation in a suspension of CDG in the ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate. The graphene-supported hybrid nanoparticles were shown to be active and could be re-used at least 10 times as catalysts for the hydrogenation of cyclohexene and benzene under organic-solvent-free conditions with constant activities up to 1570 mol cyclohexane × (mol metal)⁻¹ × h⁻¹ at 4 bar and 75 °C.     

Learning task models in ill-defined domain using an hybrid knowledge discovery framework

Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given domain which then extend domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.