Automating Thought of Search: The Future of AI Planning with Large Language Models
Here is a summary of a research paper that shows how the AutoToS approach can help make search components for planning problems that don't need human feedback and are 100% accurate. See the impressive results across diverse domains.
AI planning is a complex challenge, and recent developments in large language models (LLMs) have shown promise in tackling these difficulties. Still, how to use LLMs for planning remains an open research question.
The Thought of Search (ToS) framework demonstrated the potential of using LLMs to define the search space for planning difficulties. Despite this, ToS still required human experts to give feedback, limiting its broader applicability.
We look at a new approach called AutoToS that automates the ToS process, allowing LLMs to generate high-performing search functions without any human intervention. We’ll cover the key techniques, the impressive results across multiple planning domains, and insights into the limitations of current language models.
Automating the Thought of Search Process
The core innovation of AutoToS is its use of automated unit testing to provide feedback to the language model. This guides the model towards generating sound and complete successor functions and goal tests, eliminating the need for human validation.
The AutoToS process follows four key steps:
- The initial prompt asks the LLM to provide the successor function and goal test.
2. Goal unit tests check if the goal test correctly identifies goal and non-goal states.
3. The successor function is tested for soundness, ensuring it doesn’t modify the input state and that transitions are valid.
4. (Optional) The successor function is also tested for completeness, verifying that it generates all known successors.
By running through this feedback loop, the language model refines its outputs until the search components pass all the tests, guaranteeing 100% accuracy.
Impressive Results Across Diverse Domains
The authors of this research paper evaluated AutoToS across five representative planning domains: BlocksWorld, PrOntoQA, Mini Crosswords, 24 Game, and Sokoban. Surprisingly, they achieved 100% accuracy on all of these benchmarks, using language models of varying sizes from GPT-4 to Llama.
Again, the total number of calls to the language model was typically low, often comparable to the human-in-the-loop ToS approach. This demonstrates the efficiency of the automated feedback process in guiding the model towards correct solutions.
The authors also give an analysis of the types of errors made by the language models, identifying interesting “bloopers” that reveal limitations in their reasoning abilities. These insights can inform future research on improving LLM performance on planning and search tasks.
Benefits of Automating Thought of Search
The key benefits of the AutoToS approach are:
- Eliminates the need for human experts to give feedback, enabling an expanded range of use of LLMs for planning.
- Maintains 100% accuracy on a vast number of planning domains, ensuring reliable solutions.
- Achieves high efficiency, with a low number of calls to the language model.
- Provides valuable insights into the current limitations of language models for complex reasoning tasks.
By automating the Thought of Search process, AutoToS represents a major advancement in opening up the potential of large language models for AI planning. As the field continues to grow, techniques like this will be very important for expanding the capabilities of LLMs beyond natural language tasks.
The AutoToS approach shows how automated feedback can enable language models to generate sound and complete search components for planning problems, without the need for human experts. The impressive results across multiple domains, combined with the insights into LLM limitations, make this a significant contribution to the field of AI planning.
As you explore ways to make use of large language models for your own planning and decision-making challenges, be sure to keep AutoToS in mind as a powerful technique for automating this process and achieving reliable, high-quality solutions. The lessons learned from AutoToS will no doubt change the way we make use of the power of large language models to solve complex problems.
Explore The Research Paper Here
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