  Agent-based IoT applications have recently been proposed in several domains,
such as health care, smart cities and agriculture. Deploying these applications
in specific settings has been very challenging for many reasons including the
complex static and dynamic variability of the physical devices such as sensors
and actuators, the software application behavior and the environment in which
the application is embedded. In this paper, we propose a self-configurable IoT
agent approach based on feedback-evaluative machine-learning. The approach
involves: i) a variability model of IoT agents; ii) generation of sets of
customized agents; iii) feedback evaluative machine learning; iv) modeling and
composition of a group of IoT agents; and v) a feature-selection method based
on manual and automatic feedback.
