Evolving Artificial Language through Evolutionary Reinforcement Learning

Computational simulation of language evolution provides valuable insights into the origin of language. Simulating the evolution of language among agents in an artificial world also presents an interesting challenge in evolutionary computation and machine learning. In this paper, a jungle world is constructed where agents must accomplish different tasks such as hunting and mating by evolving their own language to coordinate their actions. In addition, all agents must acquire the language during their lifetime through interaction with other agents. This paper proposes the algorithm of Evolutionary Reinforcement Learning with Potentiation and Memory (ERL-POM) as a computational approach for achieving this goal. Experimental results show that ERL-POM is effective in situated simulation of language evolution, demonstrating that languages can be evolved in the artificial environment when communication is necessary for some or all of the tasks the agents perform.

[1]  Jiri Pospichal,et al.  An Emergence of Coordinated Communication in Populations of Agents , 1999, Artificial Life.

[2]  Andrew D. M. Smith,et al.  Establishing Communication Systems without Explicit Meaning Transmission , 2001, ECAL.

[3]  Bart de Boer,et al.  Emergence of Speech Sounds in Changing Population , 1999, ECAL.

[4]  Domenico Parisi,et al.  Producer Biases and Kin Selection in the Evolution of Communication - How the Phylogenetic and the Adaptive Problems of Communication Can Be Solved , 2010, Evolution of Communication and Language in Embodied Agents.

[5]  Bruce J. MacLennan,et al.  Synthetic Ethology and the Evolution of Cooperative Communication , 1993, Adapt. Behav..

[6]  Stefano Nolfi,et al.  Evolution of Communication and Language in Embodied Agents , 2009 .

[7]  John S. McCaskill,et al.  The cultural evolution of syntactic constraints in phonology. , 2000 .

[8]  Noam Chomsky,et al.  Language and Mind , 1973 .

[9]  Angelo Cangelosi,et al.  Simulating the Evolution of Language , 2002, Springer London.

[10]  Henry Brighton,et al.  Compositional Syntax From Cultural Transmission , 2002, Artificial Life.

[11]  John Field,et al.  Language and the mind , 1968 .

[12]  B. D. Boer,et al.  Emergence of Speech Sounds in Changing Populations , 1999 .

[13]  Aditya Rawal and Janette Boughman and Risto Miikkulainen,et al.  Evolution of Communication in Mate Selection , 2014, ALIFE.

[14]  Matthew Quinn,et al.  Evolving Communication without Dedicated Communication Channels , 2001, ECAL.

[15]  Kenny Smith,et al.  The cultural evolution of communication in a population of neural networks , 2002, Connect. Sci..

[16]  Sony CSL-Paris.  Semiotic schemata : Selection units for linguistic cultural evolution , 2000 .

[17]  Stefano Nolfi,et al.  Evolution of Implicit and Explicit Communication in Mobile Robots , 2010, Evolution of Communication and Language in Embodied Agents.

[18]  James A. Reggia,et al.  Progress in the Simulation of Emergent Communication and Language , 2003, Adapt. Behav..

[19]  Dario Floreano,et al.  Evolutionary Conditions for the Emergence of Communication , 2010, Evolution of Communication and Language in Embodied Agents.