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Retrieving and reusing qualitative cases: An application in humanoid-robot soccer

机译：检索和重用定性案例：在类人机器人足球中的应用

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摘要

This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. Qualitative relations between objects, represented in terms of the EOPRA formalism, are stored as qualitative cases that are applied in the definition of new retrieval and reuse algorithms. The retrieval algorithm uses a Conceptual Neighborhood Diagram to compute the similarity between a new problem and the cases in the case base, and to select the most similar case. The reuse algorithm uses a composition algorithm to calculate the adapted position of the agents based on their frame of reference. The proposed approach was evaluated on simulation and on real humanoid robots. Results suggest that this proposal is faster than using a quantitative model with a numerical similarity measurement such as the Euclidean distance. As a result of running Q-CBR, the robots obtained a higher average number of goals than those obtained when running a metric CBR approach.
机译：本文提出了一种新的基于案例的推理（CBR）方法，称为Q-CBR，该方法使用定性空间推理理论通过空间关系对案例进行建模，检索和重用。以EOPRA形式主义表示的对象之间的定性关系存储为定性案例，这些定性案例适用于新的检索和重用算法的定义。检索算法使用概念邻域图来计算新问题与案例库中的案例之间的相似度，并选择最相似的案例。重用算法使用合成算法根据代理的参考框架计算代理的适应位置。在仿真和真实人形机器人上对提出的方法进行了评估。结果表明，该提议比使用具有数值相似性度量（例如欧几里得距离）的定量模型要快。运行Q-CBR的结果是，机器人获得的平均目标数量高于运行度量CBR方法获得的平均目标数量。

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