Li Doujie, Fan Zhongyan, Tang Wallace K S
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
PLoS One. 2017 Nov 14;12(11):e0188164. doi: 10.1371/journal.pone.0188164. eCollection 2017.
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.
命名游戏模拟了一群智能体中词汇的演变。通过游戏中的两两互动，智能体在其记忆中获取了一组用于物体命名的词汇。现有的模型局限于名称与物体之间的一对一映射。通常关注的是群体中的名称共识而非智能体的知识学习，因此通常采用简单的学习模型。然而，人类的认知系统要复杂得多，知识通常以复杂的形式呈现。因此，在这项工作中，我们扩展了智能体学习模型并设计了一个新游戏以纳入领域学习，这对于更复杂形式的知识至关重要。特别地，我们展示了一群智能体中颜色分类和命名的演变。我们将人类感知模型纳入智能体，并在领域学习中引入了两个新概念，即主观感知和阈下刺激。模拟结果表明，即使没有任何监督或先决条件，仅通过互动就能在群体中达成颜色命名系统的共识。我们的工作证实了社会互动在颜色分类中的重要性，这是人类认知中一个长期争论的话题。此外，我们的工作还展示了自主智能体中认知系统发展的可能性。
