2018/04/11 by Angeliki Lazaridou, Karl Moritz Hermann, Lazaridou, Angeliki +6 · 6 citations
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Language and cultural evolution #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.1804.03984
openalex publication_date 2018/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.