Collaborative Interactive Learning

Fundamentals in Collaborative Interactive Learning (Acronym: CIL) 

Technical systems are solving increasingly complex tasks with the help of computers. Originally, these systems had been drawn up for particular tasks and operating conditions and were limited to those during runtime. Nowadays, they are able to adapt to new situations, learn from observations and optimize themselves. For that reason, they are often called smart or intelligent. In the future, there will be more and more applications where not all of the data necessary for learning can be provided - even not for self-learning systems at time of design. A simple adaptation (e.g. of parameters) during runtime fails to be sufficient, as well. Reasons are, for instance, the required amount of data, the time needed for acquisition or financial costs and, in particular, the fact that while duration these systems are being confronted with situations not known at the time of development (situations not able to be known, inherently). What is required, hence, is a completely new kind of smart systems with a lifelong ability to learn (corresponding to the aggregate service life of the system) in uncertain and temporal variable environments. These systems need to operate intensively autonomic, by evaluating their own knowledge, independently procuring resources (humans, other systems, internet etc.) or connecting with them, rating information of others (e.g. with respect to currentness) and thereby using different machine learning methods (e.g. Collaborative Learning or Active Learning).

The aim of this project is the investigation of a class of entirely new technologies for the development of systems outlined above and which we identify as Collaborative Interactive Learning (CIL). These machine learning methods are ‘collaborative’ in the sense that several systems cooperate among themselves and with humans, in order to mutually solve problems (including those not capable of being solved on their own). Also, they are ‘interactive’ as there will be an actively animated and regular flow of knowledge and information - not only between these technical systems but also between systems and humans in various ways. Further, we differentiate between a dedicated (D-CIL) and an opportunistic version (O-CIL) of CIL. Concerning D-CIL, processes of learning as well as tasks and groups of humans and systems involved are well-defined. Concerning O-CIL, on the other hand, tasks are variable and groups are open for diverse participants. In O-CIL, systems use all sources of information even if they are quite uncertain and possibly only sporadically available. The scientific leading question of the project is thereby defined by its necessity to develop and research entirely new concepts, technologies and mechanisms for CIL (or D-CIL and O-CIL) in several scientific disciplines. Potential applications of D-CIL or O-CIL have been identified in many areas: cyber-physical systems that are learning from each other, teams of autonomic robots, cooperating autonomic vehicles, distributed systems for intrusion detection in computer networks, design of cooperation mechanisms for the solution of tasks employing processes of Collaboration Engineering, Crowdsourcing in order to use an expertise of an indefinite mass of people etc.

• FG Maschinelles Lernen (Prof. Dr. Bernhard Sick) – University of Kassel
• FG Wirtschaftsinformatik (Prof. Dr. Jan Marco Leimeister) – University of Kassel
• FG Wissensverarbeitung (Prof. Dr. Gerd Stumme) – University of Kassel

• Fachgebiet Eingebettete Systeme (Prof. Dr. Paul Lukowicz) – Deutsches Forschungszentrum für Künstliche Intelligenz & TU Kaiserslautern
• Fachgebiet Mensch-Maschine-Schnittstelle (Prof. Dr. Albrecht Schmidt) – University of Stuttgart
• Fachgebiet Sozioinformatik (Prof. Dr. Katharina Anna Zweig) – TU Kaiserslautern

The project "Fundamental Collaborative Interactive Learning" is funded by the University of Kassel (funding program for further profiling of the University of Kassel from 2017 to 2022 – Line "Zukunft").

• Prof. Dr. Jan Marco Leimeister
• Dr. Sarah Oeste-Reiß