arXiv:2606.01708v1 Announce Type: new Abstract: We study fixed-confidence best-action identification (BAI) in stochastic minimax trees. This problem is increasingly relevant in modern AI planning, where deep minimax search and Monte Carlo Tree Search (MCTS) with language model long rollouts face a fundamental tradeoff: heuristic evaluations are cheap but biased, while accurate rollouts are reliable but prohibitively expensive. We propose 2FFS, a two-fidelity tree-search algorithm that brings multi-fidelity flat bandit ideas into trees. The algorithm combines minimax-style fast expansion with M
The increasing complexity and computational cost of AI planning and decision-making, particularly in large language models and Monte Carlo Tree Search, necessitate more efficient algorithms to bridge the gap between quick, biased heuristics and slow, accurate simulations.
This research offers a significant advancement in optimizing AI search algorithms, potentially leading to more efficient, robust, and cost-effective AI systems for complex planning tasks, impacting sectors from robotics to strategic simulations.
The introduction of two-fidelity search algorithms like 2FFS changes how AI systems balance speed and accuracy in decision-making, allowing for more nuanced and adaptable strategies in computationally constrained environments.
- · AI algorithm developers
- · Robotics companies
- · Gaming industry
- · Defense and aerospace (AI planning)
- · Developers relying solely on high-cost, high-fidelity simulations without optimi
- · Systems with poor heuristic functions
More sophisticated and computationally efficient AI decision-making will become accessible for complex tasks.
This efficiency could accelerate the development and deployment of autonomous AI agents in various applications.
Improved AI planning capabilities might lead to breakthroughs in areas requiring long-horizon strategic reasoning, such as scientific discovery or complex logistics.
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Read at arXiv cs.LG