The Vision of Organic Computing Systems
Organic Computing (short: OC) has emerged recently as a challenging vision for future information processing systems. It is based on the insight that we will soon be surrounded by systems with massive numbers of processing elements, sensors, and actuators. Many of those systems will be autonomous and due to the complexity it will be infeasible to monitor and control them entirely from external observations. Instead they must monitor, control, and adapt themselves. To do so, these systems must be aware of themselves and their environment, communicate, and organize themselves in order to perform the actions and services required.
The presence of networks of intelligent systems in our environment opens up fascinating application areas but, at the same time, bears the problem of their controllability. Hence, we have to construct these systems – which we increasingly depend on – as robust, safe, flexible, and trustworthy as possible. In order to achieve these goals, our intelligent technical systems must act more autonomously and they must exhibit life-like (organic) properties. Hence, an OC system is a technical system that adapts dynamically to the current conditions of its environment. It is self-organizing, self-configuring, self-healing, self-protecting, self-explaining, and situation-aware.
Central aspects of OC systems are inspired by an analysis of information processing in biological systems. Within short time, OC became a major research activity in Germany and worldwide.
Knowledge Exchange Within Intelligent Distributed Systems
In our work in the field of OC we focus on intelligent distributed systems such as teams of robots, smart sensor networks, or multi-agent systems. Often, the nodes of such a system have to perform the same or similar tasks, or they even have to cooperate to solve a given problem. Typically, these nodes know how to observe their local environment and how to react on certain observations, for instance, and this knowledge is represented by (symbolic) rules. However, many environments are dynamic. That is, new rules become necessary, old rules become obsolete, or rules change slightly over time (concept drift). That implies that really intelligent nodes (robots, smart sensors, agents, etc.) should adapt on-line to their environment by means of certain machine learning techniques.
Typically, nodes exchange information about what they observe in their environment in order to collaborate. We refer to this kind of knowledge as descriptive knowledge. We claim that the rules that are adapted or learned on-line (we call this functional knowledge) are more abstract and often more valuable than descriptive knowledge. Furthermore, in the case of a dynamic environment descriptive knowledge may be inadequate or even wrong to describe novel or changing phenomena. That is, an observation in the input space of one node might be misinterpreted by another node when the models that represent functional knowledge are different, for instance. Therefore, organic nodes should exchange functional knowledge instead of or in addition to descriptive knowledge. The advantages are obvious:
To realize rule exchange in intelligent distributed systems, we address, amongst others, the following research issues:
Together with partners from other universities we also address the following issues in the field of OC: