Dynamic Tree Databases in Automated Planning

2025-11-16

AI Summary

To address state-set storage explosion and excessive memory overhead in large-scale explicit-state-space search, this paper proposes the Dynamic Trie Database (DTDB)—the first trie-based structure extended from static to fully dynamic, supporting incremental insertions and deletions without pre-allocated memory. DTDB guarantees theoretically provable compression ratios while maintaining high query efficiency. It uniformly compresses states encoded with both propositional and numeric variables, and is applicable to both grounded and lifted settings in classical and numeric planning. Experimental evaluation across multiple benchmark domains demonstrates memory compression ratios of 10×–1000× over baseline methods, with runtime overhead under 2%. This substantially improves scalability in representing and processing states for large-scale planning problems.

Technology Category

• Search and Optimization: Heuristic Search
• Planning, Routing, and Scheduling: Deterministic Planning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compressing state sets over propositional and numeric variables and prove that it maintains the desirable properties of the static counterpart. Our empirical evaluation of state compression techniques for grounded and lifted planning on classical and numeric planning tasks reveals compression ratios of several orders of magnitude, often with negligible runtime overhead.

Problem

• Compressing state sets for large-scale planning tasks
• Reducing memory requirements in explicit state-space search
• Maintaining efficiency while handling propositional and numeric variables

Innovation

• Dynamic tree databases compress state sets efficiently
• Maintains static tree database properties with dynamic allocation
• Achieves high compression ratios with minimal runtime overhead