Summary of Game Agent Driven by Free-form Text Command: Using Llm-based Code Generation and Behavior Branch, By Ray Ito et al.
Game Agent Driven by Free-Form Text Command: Using LLM-based Code Generation and Behavior Branch
by Ray Ito, Junichiro Takahashi
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 text command control system for game agents can understand natural language commands expressed in free-form, unlike current technologies limited to predefined format commands. The system uses a large language model (LLM) for code generation to interpret and transform these commands into behavior branch, which facilitates execution by the game agent. This study demonstrates the system’s ability to understand and carry out natural language commands within a Pokémon game environment, confirming its effectiveness in real-time language interactive game agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for game agents to understand commands using natural language. Imagine playing a game like Pokémon, but instead of choosing actions from menus, you can give the agent simple commands, like “Catch Pikachu” or “Use Fire Blast”. The proposed system makes this possible by translating your words into instructions that the game agent can follow. |
Keywords
» Artificial intelligence » Large language model
