Summary of Caap: Context-aware Action Planning Prompting to Solve Computer Tasks with Front-end Ui Only, by Junhee Cho et al.
CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only
by Junhee Cho, Jihoon Kim, Daseul Bae, Jinho Choo, Youngjune Gwon, Yeong-Dae Kwon
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LLM-based agent mimics human behavior in solving computer tasks, eliminating the need for large-scale human demonstration data. It perceives its environment through screenshot images and uses an LLM to process them, executing keyboard and mouse operations on Graphical User Interface (GUI) without pre-provided APIs. The agent achieves an average success rate of 94.5% on MiniWoB++ and an average task score of 62.3 on WebShop, outperforming previous studies that rely solely on screen images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This LLM-based agent can solve computer tasks by mimicking human behavior. It uses screenshot images to understand the environment and then uses a Large Language Model (LLM) to process those images. The agent can execute keyboard and mouse operations without needing specific APIs or large amounts of training data. This makes it useful for automating tasks on desktops, smartphones, and other devices. |
Keywords
» Artificial intelligence » Large language model
