Providing route directions is a complicated interaction. Utterances are combined with gestures and pronounced with appropriate timing. This study proposes a model for a robot that generates route directions by integrating three important crucial elements: utterances, gestures, and timing. Two research questions must be answered in this modeling process. First, is it useful to let robot perform gesture even though the information conveyed by the gesture is given by utterance as well? Second, is it useful to implement the timing at which humans speaks? Many previous studies about the natural behavior of computers and robots have learned from human speakers, such as gestures and speech timing. However, our approach is different from such previous studies. We emphasized the listener's perspective. Gestures were designed based on the usefulness, although we were influenced by the basic structure of human gestures. Timing was not based on how humans speak, but modeled from how they listen. The experimental result demonstrated the effectiveness of our approach, not only for task efficiency but also for perceived naturalness.

In this paper, we propose a Hetero Chaotic Associative Memory for Successive Learning with Multi-Winners Competition (HCAMSL-MW). The proposed model is based on a Hetero Chaotic Associative Memory for Successive Learning (HCAMSL) and the Multi Winners Self-Organizing Neural Network (MWSONN). In most of the conventional neural network models, the learning process and the recall process are divided, and therefore they need all information to learn in advance. However, in the real world, it is very difficult to get all information to learn in advance. So we need the model whose learning and recall processes are not divided. As such model, although some models have been proposed, most of them can deal with only auto-associations. In contract, although the conventional HCAMSL can deal with hetero-associations, its storage capacity is small. In the proposed HCAMSL-MW, the storage capacity is improved by the internal pattern generation based on the multi-winners competition. We carried out a series of computer experiments and confirmed that the storage capacity of the proposed HCAMSLMW is larger than that of the conventional HCAMSL.