定性推論技法を用いた誤り可視化シミュレーションの制御

DOI Web Site 被引用文献12件 参考文献22件

書誌事項

タイトル別名
  • Management of Error-Based Simulation Using Qualitative Reasoning Techniques
  • テイセイ スイロン ギホウ オ モチイタ アヤマリ カシカ シミュレーション

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<p>It is important for a student to understand that he/she made an error in problem solving in order to correct and prevent it from being repeated. We previously proposed a framework of a simulation which reflects an error a student made in solving a mechanics problem. Irregular and unnatural behavior of mechanics objects rcflecting an error helps a student understand that his/her solution is erroneous, because it visualizes what would happen based on his/her erroneous solution. We call the simulation "Error-Based Simulation (EBS)" and such a visualization "Error-Visualization". We have implemented a generator of EBS (EBS-generator) and evaluated the effectiveness of EBS on some examples of erroneous solution through an experiment. However, EBS isn't always effective for Error-Visualization. When EBS has only a quantitative difference from a normal simulation (NS), it isn't effective for a student to understand an error. EBS should have a qualitative difference from NS to be effective. Therefore, it is very important to diagnose the difference between EBS and NS in order to make use of EBS effectively for Error-Visualization. In this paper, we propose a framework for managing EBS and a method of its implementation based on qualitative reasoning techniques. The framework is based on an assumption that in order for EBS to be effective for Error Visualization, it should have a qualitative difference from NS in an object's velocity or in the ratio of an object's velocity's change to a parameter's change. The module which manages EBS is called EBS-manager. Its error management procedure consists of two phases. In Phase 1, by using qualitative simulation, behaviors of EBS and NS are predicted and then compared with each other. When a qualitative difference is found, EBS-manager judges that the EBS is effective. When a qualitative difference cannot be found, it proceeds to Phase 2. In Phase 2, by using comparative analysis, EBS-manager tries to find a parameter of which perturbation causes a qualitative difference between FBS and NS. When such a parameter is found, EBS-manager judges that the EBS with the perturbation is effective. When such a parameter cannot be found, EBS-manager judges that EBS isn't effective. We have implemented the EBS-manager and evaluated its effectiveness through an experiment. In this paper, we also discuss the result and outline our future work.</p>

収録刊行物

  • 人工知能

    人工知能 12 (2), 285-296, 1997-03-01

    一般社団法人 人工知能学会

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