Complex Task

Complex task solving in AI focuses on enabling artificial agents to successfully complete multifaceted, real-world challenges that require advanced reasoning, planning, and interaction with dynamic environments. Current research emphasizes developing frameworks that decompose complex tasks into manageable sub-tasks, often leveraging large language models (LLMs) in conjunction with techniques like chain-of-thought prompting, retrieval-augmented generation, and multi-agent systems. These advancements are significant because they improve the capabilities of AI systems to handle intricate problems across diverse domains, from healthcare and robotics to knowledge work and scientific discovery.