Self-managed decentralised systems using K-Components and collaborative reinforcement learning - CAHILL, VINNY (ACM, 2004)
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.
Proceedings of 1st ACM SIGSOFT Workshop on Self-Managed Systems (WOSS '04),
1st ACM SIGSOFT Workshop on Self-Managed Systems (WOSS '04),
PUBLISHED
Newport Beach, CA, USA
Workshop on Self-healing systems
Keywords: Computer Science