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Agent S
Overview :
Agent S is an open agent framework designed for autonomous interaction with computers through a graphical user interface (GUI). It transforms human-computer interaction by automating complex, multi-step tasks. The framework introduces an experience-enhanced hierarchical planning approach that leverages online network knowledge and narrative memory, extracting high-level experiences from past interactions to decompose complex tasks into manageable subtasks and provide step-by-step guidance using situational memory. Agent S continuously optimizes its actions and learns from experience, achieving adaptive and effective task planning. In the OSWorld benchmark, Agent S outperformed the baseline with a success rate increase of 9.37% (an 83.6% relative improvement), demonstrating extensive versatility in the WindowsAgentArena benchmark.
Target Users :
The target audience for Agent S includes professionals and casual users who need to automate complex, multi-step tasks, especially those who frequently interact with computers in their daily lives and work. By providing experience-enhanced planning and autonomous interaction capabilities, it helps users complete tasks more efficiently, reduce repetitive work, and boost productivity.
Use Cases
Automating the process of deleting email accounts.
Executing complex software operations across different operating systems.
Engaging in autonomous interaction with computers via a graphical user interface (GUI) to perform multi-step tasks.
Features
Experience-enhanced hierarchical planning: Learning from external knowledge searches and internal experience retrieval to facilitate efficient task planning and subtask execution.
Agent-Computer Interface (ACI): Based on multimodal large language models, enhancing the reasoning and control capabilities of GUI agents.
Self-assessment module: Creating a feedback loop by storing subtask and complete task trajectories in narrative and situational memory.
Self-supervised exploration and continuous memory updates: Building initial narrative and situational memory through randomly generated tasks, continuously updated based on reasoning tasks.
Wide versatility across operating systems: The Agent S framework performs excellently on Windows OS without modifications.
High-performance benchmarking: The success rate of Agent S in the OSWorld test set significantly exceeds that of baseline models.
Modular analysis: Conducting ablation studies via hierarchical sampled subsets to demonstrate the effectiveness of each module.
How to Use
1. Visit the official Agent S website to understand the product overview.
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2. Choose the appropriate operating system and configuration based on your needs.
3. Apply the Agent S framework to specific tasks or workflows.
4. Utilize Agent S's hierarchical planning and ACI functionality to automate tasks.
5. Monitor task performance through a self-assessment module and make adjustments based on feedback.
6. Optimize Agent S's performance using self-supervised exploration and continuous memory updates.
7. Test the versatility of Agent S across different operating systems.
8. Analyze Agent S's performance and adjust configurations to improve efficiency based on modular analysis.
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