Self-managed decentralised systems using K-components and collaborative reinforcement learning

Components in a decentralised system are faced with uncertainty as how to best adapt to a changing environment to maintain or optimise system performance. How can individual components learn to adapt to recover from faults in an uncertain environment? How can a decentralised system coordinate the adaptive behaviour of its components to realise system optimisation goals given problems establishing consensus in dynamic environments? This paper introduces a self-adaptive component model, called K-Components, that enables individual components adapt to a changing environment and a decentralised coordination model, called collaborative reinforcement learning, that enables groups of components to learn to collectively adapt their behaviour to establish and maintain system-wide properties in a changing environment. Keywords Decentralised Self-Adaptive Systems, Collaborative Reinforcement Learning, Architectural Reflection

• Type: Conference
• Year: 2004
• Where: WOSS
• Authors: Jim Dowling, Vinny Cahill
• Keywords: Decentralised, Decentralised System, Individual Components, Software Engineering, WOSS 2004