  In this work, we develop a framework that jointly decides on the optimal
location of wireless extenders and the channel configuration of extenders and
access points (APs) in a Wireless Mesh Network (WMN). Typically, the rule-based
approaches in the literature result in limited exploration while reinforcement
learning based approaches result in slow convergence. Therefore, Artificial
Intelligence (AI) is adopted to support network autonomy and to capture
insights on system and environment evolution. We propose a Self-X
(self-optimizing and self-learning) framework that encapsulates both
environment and intelligent agent to reach optimal operation through sensing,
perception, reasoning and learning in a truly autonomous fashion. The agent
derives adequate knowledge from previous actions improving the quality of
future decisions. Domain experience was provided to guide the agent while
exploring and exploiting the set of possible actions in the environment. Thus,
it guarantees a low-cost learning and achieves a near-optimal network
configuration addressing the non-deterministic polynomial-time hardness
(NP-hard) problem of joint channel assignment and location optimization in
WMNs. Extensive simulations are run to validate its fast convergence, high
throughput and resilience to dynamic interference conditions. We deploy the
framework on off-the-shelf wireless devices to enable autonomous
self-optimization and self-deployment, using APs and wireless extenders.
