Summary of Trip-pal: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners, By Tomas De La Rosa et al.
TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners
by Tomas de la Rosa, Sriram Gopalakrishnan, Alberto Pozanco, Zhen Zeng, Daniel Borrajo
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 method, TRIP-PAL, combines the strengths of Large Language Models (LLMs) and automated planners to generate high-quality travel plans that satisfy constraints and optimize for user utility. This hybrid approach leverages LLMs’ extensive knowledge of the travel domain to translate user requests into data structures suitable for planners, which then generate coherent and constraint-satisfying travel plans. The authors demonstrate the effectiveness of TRIP-PAL in various travel scenarios, outperforming an LLM-based method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing user satisfaction criteria. A new approach called TRIP-PAL combines the strengths of Large Language Models (LLMs) and automated planners to generate travel plans. The method uses LLMs to get and translate travel information, then automated planners generate plans that guarantee constraint satisfaction and optimize for users’ utility. This hybrid approach can help people plan their trips more effectively. |
