Microblogging as a mechanism for humanrobot interaction
This paper presents a novel approach to social data analysis, exploring the use of microblogging to manage interaction between humans and robots, and presenting and evaluating an architecture that extends the use of social networks to connect humans and devices. The approach uses natural language processing  in the form of simple grammar-based techniques  to extract features of interest from textual data retrieved from a microblogging platform in real-time and generate appropriate executable code for the robot. The simple rule-based solution exploits some of the natural constraints imposed by microblogging platforms to manage the potential complexity of the interactions and create bi-directional communication. In order to evaluate the developed system, an analysis of real-time, user-generated social media data is presented. The analysis demonstrates the feasibility of producing programmes from the social media data which lead to executable actions by a front-end application  an approach of immediate relevance to web-based systems, like questionanswering engines, personal digital assistants, and smart home/office devices.
twitter humanrobot interfaces architectures for social robotics computational intelligence for knowledge acquisition social media retrieval