This article reviews two research papers that explore advanced search techniques for AI agents. One paper surveys how Large Language Models can function as reasoning agents, planning multi-step solutions and evaluating promising search branches, akin to goal-based or utility-based agents. The second paper details improvements to the A* path-planning algorithm using Weighted A* and adaptive heuristic rewards, enabling more dynamic and efficient navigation in complex environments. AI
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IMPACT Highlights how LLMs are enhancing traditional AI search algorithms, potentially leading to more sophisticated autonomous systems.
RANK_REASON The cluster discusses two academic papers on AI search algorithms and LLM-based agents. [lever_c_demoted from research: ic=1 ai=1.0]