Crowd-sourced Story Telling for Long-term Interaction

Long-term interaction is quite hard to machines since they do not have enough resources for the interaction and the repetition of behaviors. Thus, we introduce a method that can gather stories from the crowd and recommend a story to a user based on similarity-attraction. Moreover, we describe a virtual agent that can express the emotion of a story with non-verbal gesture for human-like interaction.

System architecture and data flow

Facial expressions for six emotions and neutral state

(a) GUI of the user client for interaction with a user (b) GUI for assessing a user’s personality and preferences

Conceptual Space for Multi-robot Systems

Symbolic representation and connectionist have various advantages, but they have inevitable disadvantages: frame problem, frame grounding, and difficulty in measuring similarity between objects. Especially, computing similarity is quite important in induction and learning. Thus, we suggest a memory model as well as memory update rules, and the memory model is based on Gädenfors’ the conceptual spaces compensating the problems of classical knowledge representations. The memory model is tested in a simulation environment with a mission that two heterogeneous robots equipped with different sensors search chemical weapons in an indoor environment.

Back to Portfolio