Pragmatic Communication: Bridging Neural Networks for Distributed Agents

Abstract

In this paper, an intelligence-to-intelligence communication design with a language generation scheme is studied. The concepts and features of pragmatics and pragmatic communication are first discussed and defined from a linguistic point of view: intelligence-to-intelligence communication in a certain environment, using task performance as the evaluation criterion, with the inputs of the goal and the construction of the environment, and the output of task completion. Then, we propose the “glue neural layer” (GNL) design to bridge two intelligence to form a deeper neural network for effective and efficient communication training. Based on the design of GNL, we shed light on the thoughts about the relationship between the structure of languages and neural networks. Furthermore, a neuromorphic framework of pragmatic communication is proposed to find a base for further discussion. Experiments show that GNL design can dramatically change performance. Finally, the advantage of pragmatic and several open research problems are discussed.