Boosting Numeric Planning Performance with Novelty Heuristics, Multi-Queue Search, and Portfolios

Core Concepts

Heuristic search can be boosted for numeric planning by leveraging novelty heuristics, the Manhattan distance heuristic, multi-queue search, and portfolios, which outperform state-of-the-art numeric planning approaches.

Abstract

The paper presents several techniques to improve the performance of heuristic search for numeric planning: Numeric Novelty Heuristics: Formalizes novelty heuristics for numeric planning, including the assignment feature (A) and boundary extension encoding feature (B). Defines two types of novelty heuristics: partition novelty (PN) and quantified both (QB) novelty. Demonstrates that the hadd⟨B,QB⟩ novelty heuristic is the best performing. Manhattan Distance Heuristic (hmd): Extends the goal count heuristic (hgc) to measure the error of numeric goal conditions. hmd is a simple but effective heuristic, often outperforming more sophisticated numeric planning heuristics. Multi-Queue Search: Maintains a separate queue in greedy best-first search for each heuristic, exploring them in a round-robin manner. Saves computation by avoiding redundant node expansions when using a novelty heuristic and its base heuristic. Portfolios: Combines multiple planners to maximize coverage over diverse domains. The portfolio configuration P(3h∥3n) achieves the best overall coverage, outperforming both single-queue search and the constraint-based PATTY solver. The experiments on the Numeric Track of the 2023 International Planning Competition demonstrate the effectiveness of these techniques, highlighting the complementary nature of heuristic search and constraint-based approaches for numeric planning.

Stats

The Manhattan distance heuristic (hmd) is defined as: hmd(s) = Σl∈Gp[l]s + Σc=ξ⊵0∈Gn γ(c, s) where γ(c, s) = 0 if s satisfies c and |[ξ]s| otherwise.

Quotes

The quantified both (QB) novelty function is defined as: kQBhf(⟨T⟩, s) = Σn=1kNn - ΣJ∈{Nk}φ(J, s) if ∃J ∈ {Nk}, φ(J, s), else kΣn=1Nn + ΣJ∈{Nk}ψ(J, s)

Key Insights Distilled From

Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning

by Dill... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05235.pdf

Deeper Inquiries

What other heuristic techniques could be explored to further improve numeric planning performance

To further improve numeric planning performance, exploring additional heuristic techniques can be beneficial. One approach could be to incorporate machine learning algorithms to learn heuristics from past planning experiences. By training a model on a diverse set of planning problems and their solutions, the model can learn to predict good heuristic values for new problems. This data-driven approach can potentially capture complex patterns in the planning domain that traditional heuristics may overlook. Another technique to explore is the integration of domain-specific knowledge into the heuristics. By incorporating domain-specific information, such as the structure of the problem or common patterns in solutions, the heuristics can be tailored to perform better on specific types of planning problems. This domain knowledge can guide the search process more effectively, leading to improved performance in numeric planning tasks. Additionally, investigating novel ways to combine multiple heuristics, such as ensemble methods or hierarchical approaches, could also enhance the performance of numeric planning solvers. By leveraging the strengths of different heuristics and combining their outputs intelligently, the overall search process can be guided more efficiently towards optimal or near-optimal solutions.

How can the portfolio approach be extended to automatically select the best combination of planners for a given domain

Extending the portfolio approach to automatically select the best combination of planners for a given domain involves incorporating machine learning techniques for planner selection. By training a model on a diverse set of planning domains and the performance of different planners on those domains, the model can learn to predict the most suitable combination of planners for a specific problem instance. The automatic configuration selection process can involve features such as problem characteristics, planner performance on similar domains, and historical data on planner combinations that have worked well in the past. By analyzing these features and using them as input to the machine learning model, the system can make informed decisions on which planners to include in the portfolio and how to allocate resources effectively. Furthermore, the portfolio selection process can be dynamic, adapting to the problem instance in real-time based on its characteristics and the performance of the planners during the search process. This adaptive approach ensures that the portfolio remains flexible and responsive to the changing dynamics of the planning task, leading to improved overall performance.

What are the implications of the complementary strengths of heuristic search and constraint-based approaches for the future development of numeric planning systems

The complementary strengths of heuristic search and constraint-based approaches in numeric planning systems offer promising avenues for future development. By leveraging the efficiency of heuristic search algorithms in quickly exploring the search space and guiding the search towards promising regions, combined with the robustness of constraint-based solvers in handling complex numeric constraints and logical relationships, a hybrid approach can be developed to tackle a wide range of planning problems effectively. This hybrid approach can involve dynamically switching between heuristic search and constraint-based methods based on the characteristics of the problem instance. For instance, heuristic search can be used for initial exploration and guiding the search towards feasible regions, while constraint-based solvers can be employed for detailed constraint satisfaction and optimization. Moreover, integrating machine learning techniques into this hybrid framework can further enhance its performance by learning from past planning experiences and adapting the search strategy based on the problem instance at hand. By continuously improving the system's decision-making process through learning and adaptation, the numeric planning system can become more efficient, robust, and capable of handling a diverse set of planning challenges.