Summary of From An Llm Swarm to a Pddl-empowered Hive: Planning Self-executed Instructions in a Multi-modal Jungle, by Kaustubh Vyas et al.
From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle
by Kaustubh Vyas, Damien Graux, Yijun Yang, Sébastien Montella, Chenxin Diao, Wendi Zhou, Pavlos Vougiouklis, Ruofei Lai, Yang Ren, Keshuang Li, Jeff Z. Pan
First submitted to arxiv on: 17 Dec 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 This paper introduces Hive, a comprehensive solution for selecting and planning atomic actions using natural language instructions. It operates over sets of deep learning models, scheduling and executing explainable plans that involve one or more available models to achieve the end-user’s task while respecting specific constraints. Hive can handle multi-modal inputs and outputs, enabling it to tackle complex real-world queries. The system uses a formal logic backbone empowered by PDDL operations to guarantee explainability and planning of complex chains of actions. The paper also introduces the MuSE benchmark for evaluating multi-modal capabilities of agent systems. Experimental results show that Hive outperforms other competing systems in task selection, offering transparency guarantees while adhering to user constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hive is a new way for computers to understand and follow instructions from humans. It uses special types of artificial intelligence models to make decisions and take actions. When you give it an instruction, like “Make me a sandwich,” Hive will figure out the best steps to take to complete your task while making sure it follows any rules or limits you set. This system can even handle tasks that involve different types of input or output, like pictures and text. The researchers created a special test called MuSE to see how well Hive works compared to other similar systems. They found that Hive is the best at choosing what actions to take and still gives you a clear explanation of why it made those choices. |
Keywords
» Artificial intelligence » Deep learning » Multi modal
