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Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance naturally improves with inference-time computation scaling-standard diffusion-based planners offer only…

Artificial Intelligence · Computer Science 2026-01-30 Jaesik Yoon , Hyeonseo Cho , Doojin Baek , Yoshua Bengio , Sungjin Ahn

Monte Carlo tree search (MCTS) has achieved state-of-the-art results in many domains such as Go and Atari games when combining with deep neural networks (DNNs). When more simulations are executed, MCTS can achieve higher performance but…

Artificial Intelligence · Computer Science 2020-12-16 Li-Cheng Lan , Meng-Yu Tsai , Ti-Rong Wu , I-Chen Wu , Cho-Jui Hsieh

Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free methods (e.g., genetic algorithms or evolution strategies) help…

Machine Learning · Computer Science 2019-12-24 Xiaobai Ma , Katherine Driggs-Campbell , Zongzhang Zhang , Mykel J. Kochenderfer

Monte-Carlo Tree Search (MCTS) is a fundamental sampling-based search algorithm widely used for online planning in sequential decision-making domains. Despite its success in driving recent advances in artificial intelligence, understanding…

Artificial Intelligence · Computer Science 2026-04-17 Yiyu Qian , Liyuan Zhao , Tim Miller

Large language models (LLMs) demonstrate impressive language understanding and contextual learning abilities, making them suitable for natural language processing (NLP) tasks and complex mathematical reasoning. However, when applied to…

Artificial Intelligence · Computer Science 2023-09-13 Haotian Xu

Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construct the search tree, which calls for effective…

Machine Learning · Computer Science 2020-10-06 Anji Liu , Yitao Liang , Ji Liu , Guy Van den Broeck , Jianshu Chen

Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Models (LLMs) with task information to generate dense reward…

Artificial Intelligence · Computer Science 2026-03-02 Ning Gao , Xiuhui Zhang , Xingyu Jiang , Mukang You , Mohan Zhang , Yue Deng

Lane-free traffic environments allow vehicles to better harness the lateral capacity of the road without being restricted to lane-keeping, thereby increasing the traffic flow rates. As such, we have a distinct and more challenging setting…

This study explores how to enhance the reasoning capabilities of large language models (LLMs) in knowledge base question answering (KBQA) by leveraging Monte Carlo Tree Search (MCTS). Semantic parsing-based KBQA methods are particularly…

Computation and Language · Computer Science 2025-02-20 Guanming Xiong , Haochen Li , Wen Zhao

AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural MCTS algorithm has been successful in finding near-optimal…

Artificial Intelligence · Computer Science 2021-10-12 Prashank Kadam , Ruiyang Xu , Karl Lieberherr

While Monte Carlo Tree Search (MCTS) shows promise in Large Language Model (LLM) based Automatic Heuristic Design (AHD), it suffers from a critical over-exploitation tendency under the limited computational budgets required for heuristic…

Machine Learning · Computer Science 2026-02-03 Kezhao Lai , Yutao Lai , Hai-Lin Liu

Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a…

Artificial Intelligence · Computer Science 2025-10-02 Pietro Totis , Alberto Pozanco , Daniel Borrajo

Decentralized Monte Carlo Tree Search (Dec-MCTS) is widely used for cooperative multi-agent planning but struggles in sparse or skewed reward environments. We introduce Coordinated Boltzmann MCTS (CB-MCTS), which replaces deterministic UCT…

Multiagent Systems · Computer Science 2026-03-11 Nhat D. A. Nguyen , Duong D. Nguyen , Gianluca Rizzo , Hung X. Nguyen

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, we, for the first time, propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In…

Networking and Internet Architecture · Computer Science 2022-02-08 Yuwen Qian , Kexin Sheng , Chuan Ma , Jun Li , Ming Ding , Mahbub Hassan

The inherent uncertainty in the environmental transition model of Reinforcement Learning (RL) necessitates a delicate balance between exploration and exploitation. This balance is crucial for optimizing computational resources to accurately…

Machine Learning · Computer Science 2025-05-21 Yongxin Deng , Xihe Qiu , Jue Chen , Xiaoyu Tan

We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree search (MCTS) is an effective heuristic search algorithm for…

Robotics · Computer Science 2022-07-15 Baichuan Huang , Abdeslam Boularias , Jingjin Yu

Scientific hypothesis generation is a fundamentally challenging task in research, requiring the synthesis of novel and empirically grounded insights. Traditional approaches rely on human intuition and domain expertise, while purely large…

Computation and Language · Computer Science 2025-03-26 Gollam Rabby , Diyana Muhammed , Prasenjit Mitra , Sören Auer

Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented approaches face challenges in addressing diverse queries…

Computation and Language · Computer Science 2024-10-17 Minda Hu , Licheng Zong , Hongru Wang , Jingyan Zhou , Jingjing Li , Yichen Gao , Kam-Fai Wong , Yu Li , Irwin King

Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and…

Robotics · Computer Science 2024-11-19 Junyong Kim , Hanwen Ren , Ahmed H. Qureshi

PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei Published: 06 Apr 2026, Last Modified: 06 Apr 2026 ACL 2026 Findings Conference, Area Chairs, Reviewers,…

Artificial Intelligence · Computer Science 2026-04-08 Siyuan Cheng , Bozhong Tian , YanChao Hao , Zheng Wei