Action Selection Algorithms for Autonomous System in Pervasive Environment: A Computational Approach

摘要

Ubiquitous and pervasive computing deals with the design of autonomous and adaptive systems and services that interact with the closest environment enhanced by context awareness and emergence functionalities. In this article, we investigate the relationships between the environment, the actions (services), and the selection algorithm that is guaranteed to take the system to a state that suits a stochastically changing environment. Making the assumption that peering relationships between potential actions can be specified by an affinity network, the action selection mechanism is translated into an iterative algorithm that lets each activity update its strength until it converges to a solution. In pervasive environments, where services and devices interfere with each other, the proposed action selection approach prevents unexpected and undesirable behaviors or oscillating loops in a such dynamic environment.