Deadlock Detection

In this paper we present a comparison between two novel approaches to the fundamental problem of cyclic instability in ambient intelligence. These approaches are based on two optimization algorithms, Particle Swarm Optimization (PSO) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of average accumulative oscillation, which enabled us to measure the average oscillatory behaviour of the system. PSO and MIMIC have the advantage that they do not need to analyze the topological properties of the system, in particular the loops. In order to test these algorithms we used the well-known discrete system called the game of life for 9, 25, 49 and 289 agents. It was found that PSO performed better than MIMIC in terms of the number of agents blocked. These results were confirmed using the Wilcoxon test. This novel and successful approach is very promising, and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies between them.