SibylSat: A Novel Greedy Search Approach to TOHTN Planning Using SAT as an Oracle

Основные понятия

SibylSat, a new SAT-based TOHTN planner, leverages a greedy search strategy guided by a heuristic derived from a relaxed problem encoding, leading to improved performance in runtime and plan quality compared to existing breadth-first SAT-based approaches.

Аннотация

• Bibliographic Information: Quenard, G., Pellier, D., & Fiorino, H. (2024). SibylSat: Using SAT as an oracle to perform a greedy search on TOHTN planning. arXiv preprint arXiv:2411.02035.
• Research Objective: This paper introduces SibylSat, a novel SAT-based planner for Totally-Ordered Hierarchical Task Network (TOHTN) planning, aiming to improve efficiency and plan quality compared to existing SAT-based TOHTN planners.
• Methodology: SibylSat employs a greedy best-first search strategy guided by a heuristic derived from solving a relaxed version of the planning problem, encoded as a SAT problem. This heuristic helps identify promising decompositions for expansion within the search space represented as a Path Decomposition Tree (PDT). The paper compares SibylSat's performance against state-of-the-art SAT-based TOHTN planners, Lilotane and pandaPIsatt-1iB, across IPC 2020 and IPC 2023 benchmarks using IPC score and quality score as evaluation metrics.
• Key Findings: Experimental results demonstrate that SibylSat outperforms existing SAT-based TOHTN approaches in terms of both runtime (IPC score) and plan quality (makespan) on most of the IPC benchmarks, while also solving a larger number of problems. The paper shows SibylSat's greedy search approach, coupled with the proposed heuristic function, effectively reduces the search space compared to the breadth-first approach of other SAT planners.
• Main Conclusions: The study highlights the effectiveness of integrating heuristic information into SAT-based TOHTN planning through SibylSat's greedy search strategy. The authors suggest that this approach offers a promising direction for improving the efficiency and scalability of TOHTN planning.
• Significance: This research contributes to the field of automated planning by introducing a novel approach to TOHTN planning that leverages the power of SAT solvers for both solution finding and heuristic guidance. The demonstrated performance improvements offered by SibylSat have the potential to impact various domains that rely on efficient and effective planning solutions.
• Limitations and Future Research: The authors acknowledge that SibylSat's greedy approach, while generally effective, can lead to non-termination in recursive domains. While they propose a method to ensure completeness in such cases, further exploration of alternative search strategies beyond greedy search is suggested. Additionally, investigating the integration of classical HTN heuristics for ranking and prioritizing promising areas within the search space is proposed as future work.

Статистика

SibylSat achieves a better IPC score in 19 out of 26 benchmark domains compared to other state-of-the-art SAT-based TOHTN planners.
SibylSat demonstrates better coverage and quality scores compared to other planners.
SibylSat generally reduces the number of methods developed before finding a solution DT in most domains compared to Lilotane.

Цитаты

"In this paper, we introduce SibylSat, a novel SAT-based approach that employs a greedy best-first search strategy... Our approach considerably reduces the search space and lays the groundwork for the development of new heuristics and techniques to solve HTN problems by SAT encodings."
"Experimentally, we demonstrate that our greedy search approach, coupled with a heuristic function, improves performance on most IPC benchmarks compared to state-of-the-art SAT TOHTN approaches."

Ключевые выводы из

SibylSat: Using SAT as an Oracle to Perform a Greedy Search on TOHTN Planning

by Gaspard Quen... в arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.02035.pdf

Дополнительные вопросы

How does the performance of SibylSat compare to non-SAT-based TOHTN planners, and what are the potential advantages and disadvantages of using SAT-based approaches in different planning scenarios?

While the provided excerpt focuses on comparing SibylSat with other SAT-based TOHTN planners, it doesn't offer a direct comparison with non-SAT-based TOHTN planners. However, we can extrapolate some insights based on the broader context of HTN planning and the characteristics of SAT-based approaches:
Potential Advantages of SAT-based TOHTN Planners:

Optimized Solving: SAT solvers are highly optimized for constraint satisfaction problems. Leveraging this optimization can lead to efficient plan generation, especially for problems that can be effectively translated into propositional logic.
Completeness: SAT-based approaches like SibylSat, when implemented with completeness in mind (as discussed in the paper for recursive domains), guarantee finding a solution if one exists within the search space.
Expressiveness: SAT encodings can potentially represent a wide range of planning constraints and domain-specific knowledge, although this might increase encoding complexity.
Potential Disadvantages of SAT-based TOHTN Planners:

Scaling Challenges: The size of the SAT formula can grow significantly with problem complexity, potentially leading to scalability issues for large problems.
Domain Translation: Effectively translating the nuances of a planning domain into a propositional logic formula can be challenging and might require significant domain engineering.
Heuristic Guidance: Traditional SAT solvers lack inherent heuristic guidance. While SibylSat introduces a heuristic, its effectiveness might vary across domains.
Comparison with Non-SAT-based TOHTN Planners:
Non-SAT-based TOHTN planners, such as those using heuristic search or graph-based methods, might offer advantages in scenarios where:

Domain Knowledge: Rich domain knowledge is available and can be effectively leveraged by heuristics.
Plan Flexibility: More flexible plan structures or partial ordering of actions are required.
Real-Time Planning: Fast, potentially suboptimal, planning solutions are needed in time-critical applications.
The choice between SAT-based and non-SAT-based approaches depends on the specific planning problem, the available domain knowledge, and the desired trade-off between optimality, completeness, and runtime performance.

Could the reliance on a single relaxed solution DT for expansion in SibylSat's greedy approach lead to overlooking potentially better solutions, and would a more explorative strategy be beneficial in certain situations?

You are absolutely right to point out that SibylSat's reliance on a single relaxed solution DT for expansion could lead to suboptimal exploration. The paper itself acknowledges the "greedy" nature of the algorithm.
Here's a breakdown of why this is a valid concern and when a more explorative strategy might be beneficial:
Potential Issues with Greedy Expansion:

Local Optima: By focusing solely on the first promising relaxed solution, SibylSat might get stuck in local optima, overlooking other areas of the search space that could lead to higher-quality plans.
Heuristic Limitations: The relaxed problem, while providing guidance, is still an approximation. Over-reliance on a single relaxed solution might amplify any inaccuracies in the heuristic.
Benefits of a More Explorative Strategy:

Global Exploration: Exploring multiple promising relaxed solutions could provide a more comprehensive view of the search space, increasing the chances of finding globally better plans.
Robustness to Heuristic Inaccuracies: A more explorative approach would be less sensitive to inaccuracies in the relaxed problem's heuristic, as it wouldn't solely depend on a single potentially misleading solution.
Situations Where Exploration is Beneficial:

Complex Domains: In domains with a high degree of interaction between tasks and a large number of possible decompositions, exploring multiple options becomes crucial.
Plan Quality Emphasis: When the quality of the plan (e.g., minimizing makespan) is a primary concern, a more explorative strategy can help escape local optima.
The paper briefly mentions the possibility of integrating classical HTN heuristics to rank and prioritize multiple promising areas. This suggests that the authors recognize the limitations of a purely greedy approach and are open to exploring more sophisticated search strategies.

How can the insights gained from SibylSat's heuristic-guided search be applied to other areas of AI that involve hierarchical planning or decision-making, such as robotics or game playing?

SibylSat's approach of using a relaxed problem to guide search in a hierarchical planning context offers valuable insights that can be extended to other AI areas:
Robotics:

Task and Motion Planning: In robotics, complex tasks like grasping an object or navigating a cluttered environment can be decomposed into hierarchical subtasks. SibylSat's idea of using a relaxed problem to guide the search for feasible task decompositions could be applied here. For instance, a relaxed problem might ignore obstacles in a navigation task to quickly find a high-level path, which can then be refined into a collision-free plan.
Multi-Robot Coordination: Coordinating multiple robots often involves hierarchical planning, where high-level tasks are assigned to individual robots and then decomposed into lower-level actions. SibylSat's approach could be adapted to guide the search for efficient task allocations and coordination strategies.
Game Playing:

Game Tree Search: Games like chess or Go involve searching through vast trees of possible moves. SibylSat's concept of using a relaxed problem could be applied to guide this search. For example, a relaxed game evaluation function might ignore certain pieces or rules to quickly estimate the value of different game states, guiding the search towards more promising moves.
Monte Carlo Tree Search (MCTS): MCTS methods, commonly used in game AI, could benefit from SibylSat's insights. The selection phase of MCTS, where the algorithm chooses which node to expand, could use a relaxed problem evaluation to prioritize more promising branches of the search tree.
General Hierarchical Decision-Making:

Problem Decomposition: Many complex AI problems, such as natural language processing or automated reasoning, benefit from hierarchical decomposition. SibylSat's approach highlights the value of using relaxed problem formulations to guide the search for effective decompositions.
Abstraction and Refinement: The core idea of using a relaxed problem to guide search can be generalized to other AI domains that involve abstraction and refinement. By creating simplified versions of a problem, we can obtain valuable heuristic information to guide the search for solutions in the original, more complex problem space.
In essence, SibylSat's key contribution lies in demonstrating how a SAT-based approach, often considered "blind," can be enhanced with heuristic guidance derived from a relaxed problem. This principle of using simplified problem representations to guide search in complex domains has broad applicability in AI, extending beyond the specific realm of TOHTN planning.