5 articles tagged with #heuristic-search. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers develop L-NAMOA*dr-mvh, a novel algorithm that safely integrates multi-valued heuristics with dimensionality reduction in multi-objective shortest-path problems. The breakthrough addresses theoretical correctness challenges and achieves over 10x speedups by better capturing trade-off structures in search optimization.
Researchers propose BiXDFBnB, a bidirectional depth-first branch-and-bound algorithm that efficiently applies front-to-front heuristics to longest-path problems by adapting the Single-Frontier Bidirectional Search framework. The method reduces computational overhead typically associated with bidirectional frontier management, achieving both fewer node expansions and improved runtime performance on several problem variants.
Researchers introduce GONDOR, a memory-efficient extension of Greedy Best-First Search that enables planning algorithms to operate under strict memory constraints by compressing search trees while retaining sparse anchor states. The algorithm reconstructs paths through re-searching between these states, with experiments showing consistent improvements in coverage on low-memory devices compared to standard approaches.
Researchers propose a unified framework for understanding Tree-of-Thoughts (ToT) as a classical heuristic search problem, mapping LLM reasoning to established search algorithms. The work synthesizes fragmented research across NLP and planning communities, identifying design patterns where Best-First Search suits shallow tasks while deeper reasoning benefits from lookahead-heavy strategies like DFS and MCTS.
Researchers have developed a parallel lifted planning algorithm using semi-naive Datalog evaluation that significantly accelerates classical AI planning by combining rule-level and grounding-level parallelism. The approach achieves up to 6-fold speedup on 8 cores and solves more planning tasks than existing baselines, particularly on computationally intensive grounding operations.
