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Goal-oriented semantic communication will be a pillar of next-generationwireless networks. Despite significant recent efforts in this area, most priorworks are focused on specific data types (e.g., image or audio), and theyignore the goal and effectiveness aspects of semantic transmissions. Incontrast, in this paper, a holistic goal-oriented semantic communicationframework is proposed to enable a speaker and a listener to cooperativelyexecute a set of sequential tasks in a dynamic environment. A common languagebased on a hierarchical belief set is proposed to enable semanticcommunications between speaker and listener. The speaker, acting as an observerof the environment, utilizes the beliefs to transmit an initial description ofits observation (called event) to the listener. The listener is then able toinfer on the transmitted description and complete it by adding related beliefsto the transmitted beliefs of the speaker. As such, the listener reconstructsthe observed event based on the completed description, and it then takesappropriate action in the environment based on the reconstructed event. Anoptimization problem is defined to determine the perfect and abstractdescription of the events while minimizing the transmission and inference costswith constraints on the task execution time and belief efficiency. Then, anovel bottom-up curriculum learning (CL) framework based on reinforcementlearning is proposed to solve the optimization problem and enable the speakerand listener to gradually identify the structure of the belief set and theperfect and abstract description of the events. Simulation results show thatthe proposed CL method outperforms traditional RL in terms of convergence time,task execution cost and time, reliability, and belief efficiency.

Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language