A neural network based online anthropomorphic performance decision-making approach is described for a dual-arm dulcimer playing robot. Because it is difficult to extract experiential rules manually to describe the decision behavior of a human playing a dulcimer, the proposed method relies on the self-learning function of a artificial neural network (ANN). The training data of the network consists of three types of information: the note pitch of adjacent notes, time interval in a piece of music, and decision results in actual performance processes of human beings. A decision-making approach, devised through combining the well-trained ANN with music for which performance decisions were required, is then applied. The numerical results show that, for several pieces of music with different characteristics, the accuracy and precision of the decision results are always relatively high, which verifies the practicability and good generalizability of the method.

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