A grammar inference approach for language self-adaptation and evolution in digital ecosystems

Socialization is the essential building process of any society in natural ecosystems. Effective socialization processes have been investigated for both "biotic" (human) and "abiotic" (virtual) entities, also within digital ecosystems in the perspective of common and self-adaptive languages. In this paper, we propose an approach for socialization, language self-adaptation, and evolution that enables an effective communicative interaction among digital entities acting in a digital ecosystem. The proposed method relies on an adaptable and extensible grammatical formalism, named Digital Ecosystem Grammar (DEG). This grammar enables digital entities to interpret the messages sent by other entities by using interaction, learning and evolution actions. Moreover, a grammar learning algorithm is applied to provide the self-adaptation mechanisms that allow the digital environment to adapt the interaction language according to new messages. The approach was suitable to support the characteristics of self-adaptation, context-awareness, evolvability, and semanticity of a digital ecosystem language.