Designing self-organizing systems using surrogate models and the compromise decision support problem construct

In this paper, we address the following question: How can self-organizing system designers steer the system towards expected global behaviors that satisfy multiple conflicting performance indicators? Self-organizing systems (SOS) have advantages in performing tasks in exploratory and hazardous domains that are not suitable for humans. The design of the SOS is however difficult because negative emergence with unwanted behaviors is likely to happen and multiple conflicting performance indicators need to be considered. To address this challenge, in this paper, we propose an SOS design method using surrogate models and the compromise Decision Support Problem (cDSP) construct. Surrogate models are used to capture the relationship between low-level rules or parameters and high-level emerging system performance. And the cDSP construct is used to explore “good enough” solutions (characterized by rule adoption rates) while managing the trade-offs among conflicting performance indicators. The efficacy of the proposed method is illustrated using a multi-agent box-pushing problem in the Webots simulation environment. It is shown in the results that our method leads to a 6.9% improvement in time efficiency, an 8.4% improvement in energy efficiency, and 26.2% in system reliability.