Leveraging Large Language Models and Formal Verification Tools to Rigorously Plan Domestic and International Travels

Core Concepts

A framework that enables large language models to formally formulate and solve complex travel planning problems as satisfiability modulo theory (SMT) problems, guaranteeing the satisfiability of constraints and generating valid travel plans.

Abstract

The paper proposes a framework that combines the advantages of large language models (LLMs) and satisfiability modulo theory (SMT) solvers to tackle the complex travel planning problem.

The key components of the framework are:

1. NL-JSON Translation: The LLM translates the natural language input into a JSON format that describes the travel planning problem.

2. JSON-Step Generation: The LLM generates steps to formulate the travel planning problem as an SMT problem based on the JSON description.

3. Step-Code Generation: The LLM generates the corresponding Python code to encode the problem and call the SMT solver.

4. SMT Solver: The framework executes the generated code to encode the problem and call the SMT solver. The SMT solver guarantees to find a solution if the constraints are satisfiable, and provides unsatisfiable reasons if the constraints cannot be satisfied.

5. Interactive Plan Repair: If the initial query is not satisfiable, the framework leverages the LLM's reasoning capabilities to analyze the unsatisfiable reasons, collect more information, and provide suggestions to the user to modify the constraints. The framework then updates the code based on the user's feedback and calls the SMT solver again to find a feasible plan.

The framework is evaluated on two datasets: the TravelPlanner benchmark and the authors' own UnsatChristmas dataset, which introduces new constraint types not present in TravelPlanner. The results show that the framework can reliably handle diverse human inputs, deliver formally-verified plans, and interactively modify unsatisfiable queries based on user preferences, outperforming baseline methods. The framework also demonstrates the ability to generalize to unseen constraint types.

Stats

"The travel starts from city o, travels k destination cities, and returns to o."
"The travel spans n days."
"The travel takes k + 1 transportation method for k + 1 travels from city to city."
"The travel visit n attractions, dine in 3n restaurants, and live in accommodations for n −1 nights."

Quotes

"Recent work has demonstrated that large language models (LLMs) (Brown et al., 2020; Ouyang et al., 2022; Achiam et al., 2023), with its abundant world knowledge, abilities to collect information via tools, and capabilities of reasoning, have significant potentials in solving planning problems (Huang et al., 2022a; Ahn et al., 2022; Yao et al., 2022; Song et al., 2023)."
"However, the planning scenarios of the existing LLM planning works are still limited to rather simple tasks such as household cleaning in which the agents only consider one or few constraints. Modern LLMs are not well-suited for directly solving highly complex combinatorial optimization problems with multiple levels of constraints as they generate responses based on token probabilities derived from their training data and do not inherently possess the ability to perform rigorous logical or mathematical reasoning."

Key Insights Distilled From

Large Language Models Can Plan Your Travels Rigorously with Formal Verification Tools

by Yilun Hao,Yo... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11891.pdf

Deeper Inquiries

How could the framework be extended to handle dynamic changes in travel constraints, such as flight cancellations or weather disruptions, during the execution of the travel plan?

To handle dynamic changes in travel constraints during the execution of the travel plan, the framework could be extended in the following ways:

Real-time Data Integration: Incorporate real-time data feeds from airlines, weather services, and other relevant sources to monitor changes in flight schedules, weather conditions, or other factors that could impact the travel plan.

Dynamic Constraint Updating: Implement a mechanism that allows the framework to dynamically update the constraints based on the real-time data. For example, if a flight is canceled, the framework should be able to adjust the transportation method or find alternative routes.

Automated Replanning: Develop algorithms that can automatically generate new plans or suggest modifications to the existing plan in response to dynamic changes. This could involve re-optimizing the plan based on the updated constraints.

Interactive Notifications: Enable the framework to provide real-time notifications to users about changes in the travel plan and offer alternative options or solutions to mitigate the impact of disruptions.

Historical Data Analysis: Utilize historical data to predict potential disruptions and proactively suggest contingency plans in case of unforeseen events.

By incorporating these features, the framework can adapt to dynamic changes in travel constraints and ensure the smooth execution of the travel plan even in the face of unexpected disruptions.

What are the potential limitations of using SMT solvers in terms of scalability and performance, especially for large-scale travel planning problems with a vast search space?

While SMT solvers are powerful tools for solving complex satisfiability problems, they do have limitations, especially when applied to large-scale travel planning problems with a vast search space:

Scalability: SMT solvers may struggle to scale efficiently to handle large amounts of data and complex constraints, leading to increased computational time and memory usage. As the search space grows, the solver may encounter scalability issues and struggle to find solutions within a reasonable time frame.

Complexity: Large-scale travel planning problems often involve a high degree of complexity with multiple interdependent constraints. SMT solvers may face challenges in efficiently navigating through the intricate relationships between constraints and variables, leading to longer solving times.

Optimization: SMT solvers may not always provide the most optimized solutions for large-scale problems. In travel planning, where multiple objectives and constraints need to be considered simultaneously, the solver may struggle to find globally optimal solutions and instead settle for suboptimal outcomes.

Resource Intensive: Running SMT solvers on large-scale problems requires significant computational resources, including memory and processing power. This can limit the solver's performance and scalability, especially when dealing with real-time or time-sensitive planning scenarios.

Search Space Exploration: In large-scale travel planning problems, the vast search space can lead to an exponential increase in the number of possible solutions. SMT solvers may face challenges in efficiently exploring the search space and identifying the most feasible and optimal plans.

Overall, while SMT solvers are valuable tools for solving complex problems, their limitations in scalability and performance need to be carefully considered when applied to large-scale travel planning problems.

Could the framework be adapted to incorporate user preferences and feedback in a more seamless and natural way, beyond the current interactive modification process?

Yes, the framework can be adapted to incorporate user preferences and feedback in a more seamless and natural way by implementing the following enhancements:

Personalized Recommendations: Utilize machine learning algorithms to analyze user preferences and behavior patterns, enabling the framework to provide personalized travel recommendations tailored to individual preferences.

Natural Language Understanding: Enhance the framework's natural language processing capabilities to better interpret and respond to user feedback in a conversational and intuitive manner. This can improve the user experience and make the interaction more natural.

Preference Learning: Implement algorithms that continuously learn and adapt to user feedback over time, allowing the framework to refine its recommendations based on user interactions and feedback.

Interactive Visualization: Incorporate interactive visualizations and interfaces that allow users to see the impact of their preferences on the travel plan in real-time, making the feedback process more engaging and informative.

Multi-Modal Interaction: Enable users to provide feedback through multiple modalities, such as voice commands, gestures, or text inputs, to enhance the flexibility and ease of communication with the framework.

By integrating these features, the framework can create a more user-centric and intuitive experience, making the incorporation of user preferences and feedback a seamless and natural part of the travel planning process.