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Sunday, July 19, 2020

AI Agents Learning to Communicate

Machines learning and adapting using language.   As suggested,the environment/context is also important.

AI agents can learn to communicate effectively  by University of Gothenburg

A multi-disciplinary team of researchers from Chalmers and University of Gothenburg has developed a framework to study how language evolves as an effective tool for describing mental concepts. In a new paper, they show that artificial agents can learn how to communicate in an artificial language similar to human language. The results have been published in the scientific journal PLOS ONE.

This research lies on the border between cognitive science and machine learning. There has been an influential proposal from cognitive scientists that all human languages can be viewed as having evolved as a means to communicate concepts in a near-optimal way in the sense of classical information theory. The Gothenburg researchers' method for training the artificial agents is based on reinforcement learning, which is an area of machine learning where agents gradually learn by interacting with an environment and getting feedback. In this case, the agents start without any linguistic knowledge and learn to communicate by getting feedback on how well they succeed in communicating a mental concept.

Reconstructing colors

"In our paper we have studied how agents learn to name mental concepts and communicate by playing a several rounds of a referential game consisting of a sender and a listener. We have especially focused on the color-domain which is well studied in cognitive science. The game works as follows; the sender sees a color and describes it by uttering a word from a glossary to the listener which then tries to reconstruct the color.

Both agents receive a shared reward based on how precise the listener's reconstruction was. The words in the glossary have no meaning at the outset; it is up to the agents to agree on the meaning of the words during multiple rounds of the game. We see that the resulting artificial languages are near-optimal in an information-theoretic sense and with similar properties as found in human languages," says Mikael Kågebäck, researcher at Sleepcycle, and whose Ph.D. dissertation at Chalmers contained some of the results presented in the paper. .... " 

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