Interoperable AI: Evolutionary Race Toward Sustainable Knowledge Sharing

Stefan Sarkadi, Andrea G. B. Tettamanzi, Fabien Gandon

Abstract: The advancement and deployment of artificial intelligent agents brought numerous benefits in knowledge and data gathering and processing. However, one of the key challenges in deploying such agents in an open environment like the Web is their interoperability as they currently mostly run in silos. In this article, we report on a simulation and evaluation based on evolutionary agent-based modeling to empirically test how sustainable different strategies are for knowledge sharing in open multiagent systems. Our results show the importance of translation-based approaches and the need for incentives to support these.