Task Decomposition

Task decomposition, the process of breaking down complex tasks into simpler subtasks, is a crucial area of research in artificial intelligence, aiming to improve the efficiency and robustness of AI systems. Current research focuses on leveraging large language models (LLMs) and reinforcement learning (RL) to automate this process, often employing hierarchical structures, modular architectures, and techniques like skill-based learning and contrastive learning to identify and manage subtasks. These advancements are significant because effective task decomposition enhances the capabilities of AI agents in diverse domains, from robotic manipulation and autonomous driving to natural language processing and data analysis, leading to more efficient and adaptable systems.