Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment


K. Lannelongue, M. de Milly, R. Marcucci, S. Selevarangame, A. Supizet, A. Grincourt


In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.


Machine Learning, Reinforcement Learning, Natural Language Processing


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Copyright © 2019 K. Lannelongue, M. de Milly, R. Marcucci, S. Selevarangame, A. Supizet, A. Grincourt

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