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.

中文翻译：

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数字生态系统中语言自适应和进化的语法推理方法

社会化是任何社会在自然生态系统中必不可少的构建过程。已经从通用和自适应语言的角度对“生物”（人类）和“非生物”（虚拟）实体以及数字生态系统中的有效社会化过程进行了研究。在本文中，我们提出了一种社会化、语言自适应和进化的方法，使数字生态系统中的数字实体之间能够进行有效的交流互动。所提出的方法依赖于一种适应性强且可扩展的语法形式，称为数字生态系统语法（DEG）。该语法使数字实体能够通过使用交互、学习和进化动作来解释其他实体发送的消息。而且，应用语法学习算法提供自适应机制，允许数字环境根据新消息调整交互语言。该方法适合支持数字生态系统语言的自适应、上下文感知、可进化性和语义特征。