Einblick - Robotics planning - # PDDL domain generation from natural language

Automated Generation of Consistent PDDL Domains using Large Language Models

Q: How could the proposed pipeline be extended to handle more complex PDDL constructs, such as durative actions, conditional effects, or hierarchical task decomposition?

To handle more complex PDDL constructs, such as durative actions, conditional effects, or hierarchical task decomposition, the pipeline could be extended in the following ways: Durative Actions: Modify the generation strategy to prompt the LLM for durative actions specifically, providing additional context on the duration and timing aspects of actions. Enhance the consistency checks to validate the temporal constraints and durations associated with durative actions. Conditional Effects: Update the prompting process to include conditional effects in the action descriptions, allowing the LLM to understand and generate actions with conditional outcomes. Integrate validation mechanisms to ensure that the conditional effects are correctly defined and consistent within the domain. Hierarchical Task Decomposition: Implement a hierarchical prompting approach where the LLM is guided to generate tasks at different levels of abstraction, enabling the creation of hierarchical task structures. Develop validation techniques to verify the coherence and consistency of the hierarchical task decomposition, ensuring that subtasks align with their parent tasks. By incorporating these adjustments, the pipeline can effectively handle more intricate PDDL constructs, accommodating the complexities of durative actions, conditional effects, and hierarchical task decomposition.

Q: How could the reachability analysis component be integrated more tightly into the error correction loop to provide more targeted feedback to the LLM and further automate the domain generation process?

To integrate the reachability analysis component more tightly into the error correction loop and enhance the automation of the domain generation process, the following steps can be taken: Real-time Feedback Loop: Implement a real-time feedback mechanism where the results of the reachability analysis are directly fed back to the LLM during the error correction loop. Provide specific feedback on the actions or predicates causing non-reachability, guiding the LLM to make necessary adjustments. Automated Correction Suggestions: Develop automated correction suggestions based on the reachability analysis results, offering precise recommendations to the LLM for resolving non-reachability issues. Enable the LLM to iteratively refine the domain and problem based on the feedback from the reachability analysis, streamlining the error correction process. Iterative Planning and Validation: Integrate iterative planning steps within the error correction loop, allowing the LLM to generate and validate plans based on the corrected domain and problem. Continuously validate the reachability of goals in the generated plans, ensuring that the corrections made by the LLM lead to executable and reachable solutions. By tightly coupling the reachability analysis with the error correction loop and providing targeted feedback to the LLM, the pipeline can automate the domain generation process more effectively, improving the quality and correctness of the generated PDDL models.

Q: What other types of consistency checks or validation techniques could be incorporated to further improve the quality of the generated PDDL domains and problems?

In addition to the existing consistency checks and validation techniques, the pipeline could incorporate the following methods to enhance the quality of the generated PDDL domains and problems: Semantic Consistency Checks: Introduce checks for semantic consistency to ensure that the relationships between actions, predicates, and objects align with the intended domain semantics. Verify that the logical implications of actions and predicates are coherent and consistent throughout the domain description. Domain-Specific Constraints: Include domain-specific constraint validation to enforce rules and restrictions unique to certain planning domains, enhancing the accuracy and relevance of the generated models. Validate constraints related to domain-specific properties, spatial configurations, or temporal dependencies to prevent inconsistencies. Plan Validation: Extend the validation process to include plan validation techniques that assess the feasibility and optimality of the generated plans in achieving the specified goals. Verify the correctness of the generated plans by simulating their execution and evaluating their effectiveness in reaching the desired outcomes. By incorporating these additional consistency checks and validation techniques, the pipeline can further improve the robustness and reliability of the generated PDDL domains and problems, ensuring their suitability for effective planning and execution.

Kernkonzepte

Large Language Models can transform natural language domain descriptions into plausible PDDL markup, but ensuring consistency within the generated domains remains challenging. This paper presents a novel concept to significantly improve the quality of LLM-generated PDDL models by performing automated consistency checking during the generation process.

Zusammenfassung

The paper focuses on utilizing Large Language Models (LLMs) for the automated generation of PDDL (Planning Domain Definition Language) domains and problems from natural language descriptions.

The key steps in the proposed pipeline are:

Generating a textual goal plan: The initial user input is used by the LLM to generate a richer textual description of the problem, including the initial state, goal state, and a sequence of actions to reach the goal.
Generating domain markup: The textual description is then used to generate a JSON-based markup, which is syntactically similar but different from PDDL. This step aims to reduce syntax-specific errors that can occur when directly generating PDDL.
Initial consistency checks: The generated JSON markup undergoes a series of consistency checks to identify issues such as wrong parameter types, undeclared parameters, predicate mismatches, etc. The errors found are then provided back to the LLM for correction.
Error correction loop: The LLM is prompted with the identified errors and suggestions for resolving them. This back-prompting process continues until the domain and problem are free of the detected inconsistencies.
Reachability analysis: Finally, the corrected PDDL domain and problem are analyzed for reachability of the goal state. This step identifies issues such as actions that are not involved in the plan or predicates that are missing from the initial state.

The authors evaluate the proposed pipeline on several classical and custom planning domains, including logistics, gripper, tyreworld, household, and pizza cooking. The results show that the framework can generate executable PDDL domains and problems for simpler scenarios, but more complex domains still present challenges in terms of consistency and reachability.

The key contribution of this work is the integration of consistency checks and reachability analysis techniques into the LLM-based generation process, which helps reduce the amount of errors in the generated domains and improves their usability for planning.

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arxiv.org

Statistiken

The paper does not contain any specific numerical data or statistics to support the key claims. The results are presented in a qualitative manner, describing the performance of the proposed pipeline on different planning domains.

Zitate

The paper does not contain any direct quotes that are particularly striking or support the key arguments.

Wichtige Erkenntnisse aus

Generating consistent PDDL domains with Large Language Models

by Pavel Smirno... um arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07751.pdf

Generating consistent PDDL domains with Large Language Models

Tiefere Fragen

How could the proposed pipeline be extended to handle more complex PDDL constructs, such as durative actions, conditional effects, or hierarchical task decomposition?

To handle more complex PDDL constructs, such as durative actions, conditional effects, or hierarchical task decomposition, the pipeline could be extended in the following ways:

Durative Actions:

Modify the generation strategy to prompt the LLM for durative actions specifically, providing additional context on the duration and timing aspects of actions.
Enhance the consistency checks to validate the temporal constraints and durations associated with durative actions.

Conditional Effects:

Update the prompting process to include conditional effects in the action descriptions, allowing the LLM to understand and generate actions with conditional outcomes.
Integrate validation mechanisms to ensure that the conditional effects are correctly defined and consistent within the domain.

Hierarchical Task Decomposition:

Implement a hierarchical prompting approach where the LLM is guided to generate tasks at different levels of abstraction, enabling the creation of hierarchical task structures.
Develop validation techniques to verify the coherence and consistency of the hierarchical task decomposition, ensuring that subtasks align with their parent tasks.

By incorporating these adjustments, the pipeline can effectively handle more intricate PDDL constructs, accommodating the complexities of durative actions, conditional effects, and hierarchical task decomposition.

How could the reachability analysis component be integrated more tightly into the error correction loop to provide more targeted feedback to the LLM and further automate the domain generation process?

To integrate the reachability analysis component more tightly into the error correction loop and enhance the automation of the domain generation process, the following steps can be taken:

Real-time Feedback Loop:

Implement a real-time feedback mechanism where the results of the reachability analysis are directly fed back to the LLM during the error correction loop.
Provide specific feedback on the actions or predicates causing non-reachability, guiding the LLM to make necessary adjustments.

Automated Correction Suggestions:

Develop automated correction suggestions based on the reachability analysis results, offering precise recommendations to the LLM for resolving non-reachability issues.
Enable the LLM to iteratively refine the domain and problem based on the feedback from the reachability analysis, streamlining the error correction process.

Iterative Planning and Validation:

Integrate iterative planning steps within the error correction loop, allowing the LLM to generate and validate plans based on the corrected domain and problem.
Continuously validate the reachability of goals in the generated plans, ensuring that the corrections made by the LLM lead to executable and reachable solutions.

By tightly coupling the reachability analysis with the error correction loop and providing targeted feedback to the LLM, the pipeline can automate the domain generation process more effectively, improving the quality and correctness of the generated PDDL models.

What other types of consistency checks or validation techniques could be incorporated to further improve the quality of the generated PDDL domains and problems?

In addition to the existing consistency checks and validation techniques, the pipeline could incorporate the following methods to enhance the quality of the generated PDDL domains and problems:

Semantic Consistency Checks:

Introduce checks for semantic consistency to ensure that the relationships between actions, predicates, and objects align with the intended domain semantics.
Verify that the logical implications of actions and predicates are coherent and consistent throughout the domain description.

Domain-Specific Constraints:

Include domain-specific constraint validation to enforce rules and restrictions unique to certain planning domains, enhancing the accuracy and relevance of the generated models.
Validate constraints related to domain-specific properties, spatial configurations, or temporal dependencies to prevent inconsistencies.

Plan Validation:

Extend the validation process to include plan validation techniques that assess the feasibility and optimality of the generated plans in achieving the specified goals.
Verify the correctness of the generated plans by simulating their execution and evaluating their effectiveness in reaching the desired outcomes.

By incorporating these additional consistency checks and validation techniques, the pipeline can further improve the robustness and reliability of the generated PDDL domains and problems, ensuring their suitability for effective planning and execution.