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The introduction of a hierarchical memory structure into a cascade associative memory model for storing hierarchically correlated patterns improves the storage capacity and the size of the basins of attraction remarkably. A learning algorithm groups descendants (second-level patterns) according to their ancestors (first-level ones), and organizes the memory(More)
In conventional models for storing hierarchically correlated patterns, correlations between ancestors (first-level patterns) and their descendants (second-level ones) are assumed to be uniform, so that the descendants are distributed around their ancestors with equal distances. However, this assumption might be unnatural. We believe that objects are encoded(More)
It is difficult to design robotic playmates for introverted children. Therefore, we examined how a robot should play with such shy children. In this study, we hypothesized and tested an effective play strategy for building a good relationship with shy children. We conducted an experiment with 5- to 6-year-old children and a humanoid robot teleoperated by a(More)
In this paper, we propose a novel method for a robot to detect robot-directed speech, that is, to distinguish speech that users speak to a robot from speech that users speak to other people or to themselves. The originality of this work is the introduction of a multimodal semantic confidence (MSC) measure, which is used for domain classification of input(More)
In this paper, we propose a novel method to detect robot-directed (RD) speech that adopts the Multimodal Semantic Confidence (MSC) measure. The MSC measure is used to decide whether the speech can be interpreted as a feasible action under the current physical situation in an object manipulation task. This measure is calculated by integrating speech, image,(More)