QoS-Aware Agent Capabilities Composition in HARMS Multi-agent Systems

Abstract

With the increasing adoption of Internet of Things (IoT) technologies, the number of agents offering equivalent capabilities is increasing more and more. The services of these capability equivalent agents may have different Quality of Service (QoS) levels. Therefore, the selection of the most appropriate services that best match some given requirements becomes a challenging issue in the HARMS (Human, Agent, Robot, Machine, Sensor) multi-agent systems. In this paper, a Social Group optimization-based QoS-aware agents services Composition Algorithm (SG-QCA) is proposed to enable HARMS interaction layer with the capability of composing agents services in large-scale IoT services environments. The simulation results show that for both randomly generated and real datasets, the proposed approach is scalable and achieves a near-to-optimal composition in a reduced composition time compared to other services composition approaches.