PCMCR: A Novel Conflict Resolution Strategy based on Possibility Theory for Human Activity Recognition

Abstract

DCR is a Distributed Collaborative Reasoning multi-agent model that aims to recognize human activities in smart homes from distributed, heterogeneous and dynamic sensor data. In this model, distributed agents with diverse classifiers, detect sensor stream data, make local predictions, communicate and collaborate to identify current activities. Conflict resolution strategies are applied to generate one final predicted activity when the local predicted activity of an agent is different from received predicted activities of other agents. In this paper, a possibilistic conflict resolution strategy, PCMCR, is proposed. Possibility theory is particularly efficient in combining multiple agents predictions that can be incomplete, uncertain, and conflicting. The PCMCR strategy deals with uncertainty factor which can be presented in the predictions of poor agents. It can take advantage of the complementary information given by each agent, even the weak ones. We experimentally test this strategy by performing an evaluation study on Aruba dataset. The obtained results indicate an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the existing conflict resolution method max-trust of DCR, to the centralized model and to an existing distributed system.