Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate. However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners and thus has to overcome a transmission bottleneck.
 In this work, we insert such a bottleneck in a referential game, by introducing a changing population of agents in which new agents learn by playing with more experienced agents. We show that mere cultural transmission results in a substantial improvement in language efficiency and communicative success, measured in convergence speed, degree of structure in the emerged languages and within-population consistency of the language. However, as our core contribution, we show that the optimal situation is to co-evolve language and agents. When we allow the agent population to evolve through genotypical evolution, we achieve across the board improvements on all considered metrics. These results stress that for language emergence studies cultural evolution is important, but also the suitability of the architecture itself should be considered. Human languages show a remarkable degree of structure and complexity, and how such a complex system can have emerged is still an open question. One concept frequently named in the context of language evolution is cultural evolution. Unlike animal languages, which are taken to be mostly innate, human languages must be re-acquired by each individual BID29 BID10 . This pressures them to fit two constraints that govern their cross-generational transmission: They must be learnable by new language users, and they must allow effective communication between proficient language users (see, e.g. BID31 .In the recent past, computational studies of language emergence using referential games (see Section 2.1 for a review) has received a new wave of attention. These studies are motivated by the second constraint, that language is used to communicate. The first constraint, on the other hand, is in this framework not considered: language is not transmitted from agent to agent and there is thus no need for agents to develop languages that would survive a transmission bottleneck. 1 In this work, we introduce a transmission bottleneck in a population of agents playing referential games, implicitly modelling cultural evolution. However, merely adding a transmission bottleneck is not enough. Since the types of language that may emerge through passing this bottleneck are not just dependent on the existence of a bottleneck, but also on the shape of the bottleneck, which is determined by the biases of the architecture of the agents playing the game (their genotypical design). If the genotypical design of those agents is not suitable to solve this task through communication, they will -at best -converge to a language that doesn't allow for effective communication or is difficult to learn for every new agent or -at worst -not converge to an appropriate culturally transmittable language at all. In this work, we therefore study the co-evolution of language and architecture in a referential games.To this end, we introduce the Language Transmission Engine that allows to model both cultural and genetic evolution in a population of agents. We demonstrate that the emerging languages ben-efit from including cultural transmission as well as genetic evolution, but the best results are achieved when both types of evolution are included and languages and agents can co-evolve.2 Related Work In this paper, we introduced a language transmission bottleneck in a referential game, where new agents have to learn the language by playing with more experienced agents. To overcome such bottleneck, we enabled both the cultural evolution of language and the genetic evolution of agents, using a new Language Transmission Engine. Us- ing a battery of metrics, we monitored their respective impact on communication efficiency, degree of linguistic structure and intra-population language homogeneity. While we could find important differences in between cultural evolution strategies, it is when we included genetic evolution that agents scored best. In a second experiment, we paired new agents with evolved languages and agents and again confirmed that, while cultural evolution makes a language easier to learn, coevolution leads to the best communication.In future research, we would like to apply the Language Transmission Engine on new, more complex tasks and further increase our understanding of the properties of the emerged languages and architectures. Additionally, we would like to investigate other neuro-evolution techniques and apply them on different search spaces.
We enable both the cultural evolution of language and the genetic evolution of agents in a referential game, using a new Language Transmission Engine.
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