DBIS

In our efforts to support heterogeneous communities with the tools and structures they need, we created our flagship peer-to-peer community platform las2peer. In our latest research efforts, we developed a social bot framework, which allows for a model-driven construction and utilization of social bots in the domain of technology enhanced learning (TEL). MobSOS, the community success measurement framework of las2peer, is used to aggregate distributed data from the microservices across the network and send it to the social bots, which use machine-learning technologies (e.g. RNN with TensorFlow) to compute their answer. As interfaces for interaction with the bot, we currently use both the learning application themselve (e.g. the bot reacts by acting as a participating user in the learning scenario), as well as a chat interface via Slack. With the latest enhancements, the bot is able to react on messages within a conversational channel. As an example application to evaluate our framework, we have used our informal learning application Distributed Noracle, as well as the messenger Slack.

The goal of this thesis is to utilize chat interfaces to create social bots and to extend the existing social bot framework with a bot itself. Additionally, the framework should be evaluated using existing, las2peer powered, community applications (e.g. the Requirements Bazaar).

If you are interested in this thesis, please do not hesitate to send a message to  neumann@dbis.rwth-aachen.de.