  Goal-oriented semantic communication will be a pillar of next-generation
wireless networks. Despite significant recent efforts in this area, most prior
works are focused on specific data types (e.g., image or audio), and they
ignore the goal and effectiveness aspects of semantic transmissions. In
contrast, in this paper, a holistic goal-oriented semantic communication
framework is proposed to enable a speaker and a listener to cooperatively
execute a set of sequential tasks in a dynamic environment. A common language
based on a hierarchical belief set is proposed to enable semantic
communications between speaker and listener. The speaker, acting as an observer
of the environment, utilizes the beliefs to transmit an initial description of
its observation (called event) to the listener. The listener is then able to
infer on the transmitted description and complete it by adding related beliefs
to the transmitted beliefs of the speaker. As such, the listener reconstructs
the observed event based on the completed description, and it then takes
appropriate action in the environment based on the reconstructed event. An
optimization problem is defined to determine the perfect and abstract
description of the events while minimizing the transmission and inference costs
with constraints on the task execution time and belief efficiency. Then, a
novel bottom-up curriculum learning (CL) framework based on reinforcement
learning is proposed to solve the optimization problem and enable the speaker
and listener to gradually identify the structure of the belief set and the
perfect and abstract description of the events. Simulation results show that
the proposed CL method outperforms traditional RL in terms of convergence time,
task execution cost and time, reliability, and belief efficiency.
