Can LLMs automate mobile tasks efficiently?
MobA: A Two-Level Agent System for Efficient Mobile Task Automation
October 18, 2024
https://arxiv.org/pdf/2410.13757This paper introduces MobA, a system that uses multiple AI agents to automate tasks on mobile phones. It focuses on using a two-level agent system driven by a large language model (LLM) to understand complex instructions and interact with mobile app interfaces.
Key takeaways for LLM-based multi-agent systems:
- Two-Level Structure: MobA employs a "Global Agent" for high-level planning and a "Local Agent" for execution, similar to how the human brain delegates tasks. This makes the system more efficient and adaptable.
- Task Decomposition: MobA breaks down complex instructions into smaller sub-tasks, enabling more robust and error-resistant execution.
- Memory and Reflection: The system incorporates memory modules to learn from past experiences, and reflection mechanisms to analyze and correct mistakes during task execution.
- View Hierarchy Processing: MobA utilizes the hierarchical structure of mobile app interfaces to better understand and interact with UI elements. This reduces reliance on purely visual processing and improves efficiency.