A key issue in social intelligence design is the realization of artifacts that can fluently communicate with people. Thus, we proposed a two-layered approach to enhance a robot’s capacity of involvement and engagement. The upper layer flexibly controls social interaction by dynamic Bayesian networks (DBN) representing social interaction patterns. The lower layer improves the robustness of the system by detecting rhythmic and repetitive gestures. We designed a listener robot that can follow and record humans’ explanation on how to assemble and/or (...) disassemble a bicycle. The implementation of this system is described by assembling the key algorithms presented in this paper. (shrink)

In this article we present a parameterized model for generating multimodal behavior based on cultural heuristics. To this end, a multimodal corpus analysis of human interactions in two cultures serves as the empirical basis for the modeling endeavor. Integrating the results from this empirical study with a well-established theory of cultural dimensions, it becomes feasible to generate culture-specific multimodal behavior in embodied agents by giving evidence for the cultural background of the agent. Two sample applications are presented that make use (...) of the model and are designed to be applied in the area of coaching intercultural communication. (shrink)