Solving DCOPs in Self-optimising Multi-Agent Systems by Extending the Local Objective Functions

Several applications of Organic Computing (OC) systems as well as Autonomic Computing (AC) systems are based on self-optimising multi-agent systems, i.e. distributed autonomous devices. One of the main challenges of these is to emerge towards a global optimal system state based only on local information for each agent. In order to reach a global optimal state some agents need to avoid selfish actions and instead consider the benefits of their actions for the whole system. Choosing the action of an agent is often based on solving optimisation problems, which can be modelled as a distributed constraint optimisation problem (DCOP). This paper presents a new asynchronous approach to solve DCOP by extending only the underlying local objective function of each agent. The main benefit of this approach is the avoidance of an additional complex decision making algorithm that may interfere with the original task of an agent and reduces the scalability of the system. Exemplary, a distributed constraint optimisation problem is considered to quantify the effectiveness and computation as well as communication cost of the discussed approach.