Domain learning naming game for color categorization

Naming game simulates the evolution of vocabulary in a population of agents. Through pairwise interactions in the games, agents acquire a set of vocabulary in their memory for object naming. The existing model confines to a one-to-one mapping between a name and an object. Focus is usually put onto name consensus in the population rather than knowledge learning in agents, and hence simple learning model is usually adopted. However, the cognition system of human being is much more complex and knowledge is usually presented in a complicated form. Therefore, in this work, we extend the agent learning model and design a new game to incorporate domain learning, which is essential for more complicated form of knowledge. In particular, we demonstrate the evolution of color categorization and naming in a population of agents. We incorporate the human perceptive model into the agents and introduce two new concepts, namely subjective perception and subliminal stimulation, in domain learning. Simulation results show that, even without any supervision or pre-requisition, a consensus of a color naming system can be reached in a population solely via the interactions. Our work confirms the importance of society interactions in color categorization, which is a long debate topic in human cognition. Moreover, our work also demonstrates the possibility of cognitive system development in autonomous intelligent agents.命名游戏（Naming Game）模拟了AI智能体（Agents）种群中词汇的演化过程。通过游戏中的两两交互，智能体可在其记忆中获取一套用于物体命名的词汇集。现有模型仅限定了名称与物体间的一一映射关系，且过往研究多聚焦于种群内的名称共识达成，而非智能体的知识学习，因此往往采用简化的学习模型。然而，人类的认知系统更为复杂，知识也通常以复杂形式呈现。据此，本研究对智能体学习模型进行了拓展，并设计了全新的游戏以融入领域学习——这对于处理更复杂的知识形式至关重要。具体而言，我们演示了智能体种群中色彩分类与命名的演化过程：将人类感知模型融入智能体，并在领域学习中引入了两个全新概念，即主观感知与阈下刺激。仿真结果表明，即便无需任何监督或前置条件，种群仅通过交互即可达成色彩命名系统的共识。本研究证实了社会交互在色彩分类中的重要性——这一议题在人类认知研究中一直存在广泛争论。此外，本研究还验证了自主智能体中认知系统自主发展的可行性。