Front-to-Attractors: Modifying the Front-to-Front Heuristic in Bidirectional Search
Quick Answer
This paper shows that The front-to-attractors (F2A) heuristic significantly reduces computational costs in bidirectional search by estimating distances to a small set of attractors, achieving up to 11.2x fewer pairwise evaluations than front-to-front (F2F) heuristics and 4.8x fewer node expansions than front-to-end (F2E) heuristics.
Quick Take
The front-to-attractors (F2A) heuristic significantly reduces computational costs in bidirectional search by estimating distances to a small set of attractors, achieving up to 11.2x fewer pairwise evaluations than front-to-front (F2F) heuristics and 4.8x fewer node expansions than front-to-end (F2E) heuristics.
Key Points
- F2A maintains informativeness of F2F while reducing computational overhead.
- Estimates distance to a dynamically maintained set of attractors instead of the full frontier.
- Achieves 11.2x fewer pairwise evaluations compared to F2F heuristics.
- Results in 4.8x fewer node expansions than F2E heuristics on average.
- Evaluated across multiple domains demonstrating significant performance improvements.
Article Content
From source RSS / original summaryarXiv:2606. 07047v1 Announce Type: new Abstract: Heuristics play a central role in the performance of bidirectional search algorithms, which commonly rely on two main classes. Front-to-end (F2E) heuristics estimate the distance from a state s to the target of the search (the goal for forward search or the start for backward search). In contrast, front-to-front (F2F) heuristics estimate the distance from s to the opposite search frontier using a pairwise function h(s, s'), where s' ranges over frontier states.
Although F2F heuristics are typically more informative and therefore reduce the number of node expansions, their reliance on extensive pairwise evaluations incurs substantial computational overhead. To address this limitation, we introduce a new heuristic class, front-to-attractors (F2A), that preserves much of the informativeness of F2F while dramatically reducing its computational cost.
Rather than evaluating distances to all states on the opposite frontier, F2A estimates the distance from s to a small, dynamically maintained set of attractors in the opposite search direction. These attractors serve as a surrogate for the full frontier, enabling rich heuristic guidance at a fraction of the computational expense while maintaining the optimality guarantees offered by F2F. We evaluate F2A across multiple domains and show that it reduces the number of pairwise evaluations by up to 11.
2x compared to F2F, while achieving 4. 8x fewer node expansions than F2E on average.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
This paper addresses the sim-to-real gap for foundation model agents by framing it within a Markov Decision Process (MDP) structure. It advocates for established solutions like domain randomization to enhance agent robustness, aiming to create standardized benchmarks for reliable real-world applications.