Core Concepts
Utilizing deep learning models to simulate language emergence and communication dynamics.
Abstract
The chapter explores the use of deep learning models in simulating language evolution, focusing on communication games. It discusses the importance of computational modeling in studying language emergence and the role of agent-based systems. The content highlights the significance of neural networks, such as RNNs and Transformers, in designing communicative agents for language simulations. It also delves into key concepts like perception modules, generation modules, understanding modules, and action modules within neural networks. The text emphasizes the optimization process for training agents in communication games using reinforcement learning techniques. Additionally, it provides insights into reward functions, loss functions, gradient updates, and regularization methods to enhance training efficiency.
Stats
Several methods have been used to investigate the origin of language evolution.
Deep neural networks have achieved human-level performance in various domains.
Machine learning has rapidly developed with the advent of deep learning.
Communication games are a framework used to investigate structured communication protocols.
Neural networks are suited for modeling communicative agents with functional modules.
Quotes
"Understanding the emergence of this unique human ability has always been a vexing scientific problem due to the lack of access to the communication systems of intermediate steps of hominid evolution."
"Computer modeling can help overcome these limitations and has played a prominent role in studying language evolution for a long time."
"The sender produces a sequence of symbols to assist the receiver in completing a predetermined task."
"In reinforcement learning, rewards typically measure success without any human prior."
"Optimizing communication games involves selecting reward functions for each agent and tuning numerous parameters."