Bot Computing using the Power of Wiki Collaboration Bot computing using the power of Wiki collaboration and an experimental implementation of the bot running environment are discussed. While botnets are usually created for malicious purposes, the bot computing in this study aims to use bots for beneficial purposes. The massively parallel and persistence features of a botnet can enhance its computing power and high availability for beneficial computing. Bot computing can also enhance peopleu0027s collaboration by introducing dynamic Web pages to previously static Wiki networks. Parallel dynamic programming for solving a minimal path problem is shown as an example. Resources such as the number of bots and the number of web pages were proportional to the number of nodes, and the time to solve the minimal path problem was proportional to the number of arcs of the minimal path.

Collaborative Creation and Training of Social Bots in Learning Communities The interaction between instructors and students is one of the key concepts to improve the studentu0027s learning process. To personalize learning on a massive scale, social bots can be used as supporting technology. However, their development for virtual learning environments currently requires deep technical knowledge. This leaves learner communities relying on highlyskilled developers to generate and tailor these social bots. Participatory design, enduser development and modeldriven principles bear the potential to close this technical gap. In this paper, we propose a modeldriven approach for creating social bots. Using our framework, learners can create, train and utilize these for selfhosted virtual learning environments relying on OpenAPI specifications offered, e.g. by Blackboard. We support both retrievalbased bots that react to certain events in predefined ways, as well as generative bots by utilizing open source deep learning technologies. Our first evaluation shows the usefulness of modeldriven generation and utilization of social bots. We see the potential of this approach to move the development closer to the actual learner.