AI Agent

Graph Policy Gradients for Large Scale Unlabeled Motion Planning with Constraints

Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；multi-agent(abstract)

Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor

Jungwon Park, H. Jin Kim

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；multi-agent(abstract)

Comments 8 pages, IROS2019 accepted

Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints

Arbaaz Khan, Chi Zhang, Shuo Li, Jiayue Wu, Brent Schlotfeldt, Sarah Y. Tang, Alejandro Ribeiro, Osbert Bastani, Vijay Kumar

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；multi-agent(abstract)

PEARL: PrEference Appraisal Reinforcement Learning for Motion Planning

Aleksandra Faust, Hao-Tien Lewis Chiang, Lydia Tapia

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；multi-agent(abstract)

Comments 20 pages

Deep sequential models for sampling-based planning

Yen-Ling Kuo, Andrei Barbu, Boris Katz

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；multi-agent(abstract)

Comments Published in IROS 2018

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.CL、cs.LG

Comments 11 pages, 10 figures, EMNLP 2018 long paper

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.CL、cs.LG

Comments 11 pages, 8 figures, Accepted in ACL 2018

An Object-oriented approach to Robotic planning using Taxi domain

Aasheesh Singh

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；autonomous agent(abstract)

Comments Standard 4 page IEEE Format Submitted in IEEE-DTU Technical Journal IOTA

3D Maps Registration and Path Planning for Autonomous Robot Navigation

Konstantinos Charalampous, Ioannis Kostavelis, Dimitrios Chrysostomou, Angelos Amanatiadis, Antonios Gasteratos

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；autonomous agent(abstract)

Comments 3 pages, 6 figures, IROS'13 Workshop on Robots and Sensors integration in future rescue INformation system (ROSIN'13)

An Agent-based Manufacturing Management System for Production and Logistics within Cross-Company Regional and National Production Networks

S. Heinrich, H. Durr, T. Hanel, J. Lassig

专题命中 规划决策 ：agent(title,abstract)；planning(abstract)；multi-agent(abstract)

Planning Ahead with RSA: Efficient Signalling in Dynamic Environments by Projecting User Awareness across Future Timesteps

基于RSA的前瞻性规划：通过跨未来时间步投影用户意识实现动态环境中的高效信号传递

Anwesha Das, John Duff, Jörg Hoffmann, Vera Demberg

机构 * Saarland University（萨尔兰大学） 纠错

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.CL；autonomous agent(journal_ref)

AI总结 本文提出基于RSA的前瞻性规划方法，通过跨未来时间步投影用户意识，优化动态环境中的人机协作效率。

Comments 11 pages, 3 figures

Journal ref Proc. of the 25th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2026)

AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning

AutoTool: 面向智能体推理的动态工具选择与集成

Jiaru Zou, Ling Yang, Yunzhe Qi, Sirui Chen, Mengting Ai, Ke Shen, Jingrui He, Mengdi Wang

机构 * Nanyang Technological University（南洋理工大学） 纠错

专题命中 规划决策 ：agentic(title,abstract)；tool use(abstract)；分类 cs.CL、cs.LG

AI总结 提出AutoTool框架，通过双阶段优化（SFT+RL轨迹稳定化和KL正则化Plackett-Luce排序）使大语言模型具备动态工具选择能力，在数学、科学、代码和多模态推理等任务上平均提升6.4%-7.7%。

Comments ICML2026; Best Paper Award at ICCV 2025 Workshop on Multi-Modal Reasoning for Agentic Intelligence

Constructing the Umwelt: Cognitive Planning through Belief-Intent Co-Evolution

构建环境：通过信念-意图共演的认知规划

Shiyao Sang

机构 * Shiyao Sang（桑世尧） 纠错

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

AI总结 本文提出基于信念-意图共演的认知规划方法，通过MBCWM和TIWM模型实现环境与现实的认知一致性，提升自动驾驶规划性能并展现类人认知行为。

Comments 12 pages, 8 figures. A paradigm shift from reconstructing the world to understanding it: planning through Belief-Intent Co-Evolution

From Perception to Action: Spatial AI Agents and World Models

从感知到行动：空间AI代理与世界模型

Gloria Felicia, Nolan Bryant, Handi Putra, Ayaan Gazali, Eliel Lobo, Esteban Rojas

机构 * AtlasPro AI 纠错

专题命中 规划决策 ：AI agent(title,comments)；planning(abstract)；agentic(abstract)；分类 cs.AI、cs.LG

AI总结 本文提出了一种统一的三轴分类法，将代理能力与空间任务联系起来，强调空间定位与符号定位的区别，并指出世界模型对跨尺度安全部署的重要性。

Comments 61 pages, 742 citations, 1 figure, 3 tables. Survey paper on spatial AI agents, embodied AI, graph neural networks, and world models

ADAGE: A generic two-layer framework for adaptive agent based modelling

Benjamin Patrick Evans, Sihan Zeng, Sumitra Ganesh, Leo Ardon

专题命中 规划决策 ：agent(title,abstract)；multi-agent(abstract)；分类 cs.AI、cs.LG；autonomous agent(comments)

Comments Accepted at the 2025 International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

A Language Agent for Autonomous Driving

Jiageng Mao, Junjie Ye, Yuxi Qian, Marco Pavone, Yue Wang

专题命中 规划决策 ：agent(title,abstract)；planning(abstract)；分类 cs.AI、cs.CL

Comments COLM 2024. Project Page: https://usc-gvl.github.io/Agent-Driver/

World Value Functions: Knowledge Representation for Learning and Planning

Geraud Nangue Tasse, Benjamin Rosman, Steven James

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments Accepted at the Planning and Reinforcement Learning Workshop at ICAPS 2022. arXiv admin note: text overlap with arXiv:2205.08827

Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL

Kin-Ho Lam, Delyar Tabatabai, Jed Irvine, Donald Bertucci, Anita Ruangrotsakun, Minsuk Kahng, Alan Fern

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments This work will appear in the Proceedings of the 32nd International Conference on Automated Planning and Scheduling (ICAPS2022) https://icaps22.icaps-conference.org/papers

Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators

Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Pack Kaelbling, Shirin Sohrabi, Michael Katz

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments Equal contributions by the first two authors. This manuscript is a camera-ready version accepted in ICAPS-2022. It is significantly updated from past versions (e.g., in the ICAPS PRL (Planning and RL) workshop) with additional experiments comparing existing work (STRIPS-HGN (Shen, Trevizan, and Thiebaux 2020) and GBFS-GNN (Rivlin, Hazan, and Karpas 2019))

Differentiable Spatial Planning using Transformers

Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments Published at ICML 2021. See project webpage at https://devendrachaplot.github.io/projects/spatial-planning-transformers

Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures

Ashkan Jasour, Xin Huang, Allen Wang, Brian C. Williams

专题命中 规划决策 ：agent(title,abstract)；planning(abstract)；分类 cs.AI、cs.LG

Comments Accepted at Autonomous Robots. Author version, with 11 pages, 5 figures, 2 tables. Journal extension of "Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures" (Wang et al. RSS 2020, arXiv:2005.13458)

Learning-based Preference Prediction for Constrained Multi-Criteria Path-Planning

Kevin Osanlou, Christophe Guettier, Andrei Bursuc, Tristan Cazenave, Eric Jacopin

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments arXiv admin note: text overlap with arXiv:2108.00978

Journal ref International Conference on Automated Planning and Scheduling 2019, Workshop SPARK

Policy-focused Agent-based Modeling using RL Behavioral Models

Osonde A. Osoba, Raffaele Vardavas, Justin Grana, Rushil Zutshi, Amber Jaycocks

专题命中 规划决策 ：agent(title,abstract)；multi-agent(abstract)；分类 cs.AI、cs.LG

Comments This is a more detailed version of a paper ("Modeling Agent Behaviors for Policy Analysis via Reinforcement Learning") accepted to appear in IEEE ICMLA 2020. This also corrects an error in Fig. 7 of the original arXiv submission. Fig. 7 now specifies the right ABM architecture ("flu" instead of "tax")

Cognitive Mapping and Planning for Visual Navigation

Saurabh Gupta, Varun Tolani, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments Extended IJCV Version of the original paper at CVPR17. Project website with code, models, simulation environment and videos: https://sites.google.com/view/cognitive-mapping-and-planning/

Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions

深度研究可靠吗？误导性知识会导致错误结论

Pengyu Zhu, Lijun Li, Longju Yang, Sen Su, Jing Shao

机构 * Beijing University of Posts and Telecommunications（北京邮电大学） ； Shanghai Artificial Intelligence Laboratory（上海人工智能实验室） ； Chongqing University of Posts and Telecommunications（重庆邮电大学） 纠错

专题命中 规划决策 ：agent(summary_cn,abstract)；planning(abstract)；分类 cs.AI

AI总结 研究深度研究代理在开放信息环境中的可靠性，引入MisKnow-Agent框架生成误导性实例，通过实验发现其易因误导知识得出错误结论，验证与实际证据使用脱节，评估防御措施效果，强调需提升模型和框架层面的证据验证及纠正能力。

OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis

OralAgent: 融合推理、工具与知识的交互式牙科影像分析

Jing Hao, Siyuan Dai, Yongxin Zhang, Yuci Liang, Jiamin Wu, Jiahao Bao, Yuxuan Fan, Zanting Ye, Yanpeng Sun, Xinyu Zhang, Ming Hu, Liang Zhan, James Kit Hon Tsoi, Linlin Shen, Junjun He, Kuo Feng Hung

机构 * Faculty of Dentistry, the University of Hongkong, Hong Kong SAR, China（香港大学牙科学院，中国香港特别行政区） ； Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA（匹兹堡大学电气与计算机工程系，美国宾夕法尼亚州匹兹堡） ； Shenzhen University, China（深圳大学，中国） ； Department of Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, China（上海第九人民医院口腔颌面外科部，中国） ； Nanyang technological University, Singapore（南洋理工大学，新加坡） ； School of Biomedical Engineering, Southern Medical University, China（南方医科大学生物医学工程学院，中国） ； Singapore University of Technology and Design, Singapore（新加坡科技设计大学，新加坡） ； University of Auckland, new zealand（奥克兰大学，新西兰） ； Shanghai Artificial Intelligence Laboratory , China（上海人工智能实验室，中国） 纠错

专题命中 规划决策 ：agent(abstract)；AI agent(abstract)；tool use(abstract)；planning(abstract)

AI总结 提出首个牙科专用AI智能体OralAgent，通过集成22种视觉分析工具和368本经典牙科教科书，实现多模态推理、工具决策与知识检索的自动化框架，在多个基准上达到最优性能。

Comments 14 pages, 7 figures, 6 tables

Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations

大语言模型代理能否应对灾难？评估异构地理空间推理在紧急行动中的基准测试

Junjue Wang, Weihao Xuan, Heli Qi, Pengyu Dai, Kunyi Liu, Hongruixuan Chen, Zhuo Zheng, Junshi Xia, Stefano Ermon, Naoto Yokoya

机构 * The University of Tokyo（东京大学） ； RIKEN AIP（理化学研究所AIP） ； Waseda University（早稻田大学） ； Stanford University（斯坦福大学） 纠错

专题命中 规划决策 ：agent(abstract,abstract_cn)；tool use(abstract)；planning(abstract)；agentic(abstract)

AI总结 本文提出DORA基准测试，评估大语言模型在灾难响应中的端到端流程，揭示了灾难领域接地、工具选择瓶颈和组合脆弱性等挑战。

Comments DORA stress-tests LLM agents on real-world disaster operations that demand comprehensive orchestration of 108 specialized tools over heterogeneous geospatial data

ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering

ReCoQA：一个用于房地产问答中工具增强和多步骤推理的基准

Yindong Zhang, Wenmian Yang, Yiquan Zhang, Weijia Jia

机构 * Hong Kong Baptist University（香港 Baptist大学） ； Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学） ； Beijing Normal University（北京师范大学） 纠错

专题命中 规划决策 ：agent(summary_cn,abstract)；planning(abstract)；分类 cs.CL

AI总结 本文提出ReCoQA基准，包含29270个房地产实例，通过机器可验证的监督提升多步骤推理能力，并提出HIRE-Agent框架作为强基线，验证了层级协作在复杂现实任务中的必要性。

Comments Accepted by ACL 2026

How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Use

LLMs距离专业扑克玩家还有多远？结合代理工具使用的博弈论推理再探

Minhua Lin, Enyan Dai, Hui Liu, Xianfeng Tang, Yuliang Yan, Zhenwei Dai, Jingying Zeng, Zhiwei Zhang, Fali Wang, Hongcheng Gao, Chen Luo, Xiang Zhang, Qi He, Suhang Wang

机构 * The Pennsylvania State University（宾夕法尼亚州立大学） ； HKUST (GZ)（香港科技大学） ； Amazon（亚马逊） ； Tsinghua University（清华大学） ； Microsoft（微软公司） 纠错

专题命中 规划决策 ：tool use(title)；agentic(title)；分类 cs.AI

AI总结 本文提出ToolPoker框架，通过整合外部求解器和专业解释，提升LLMs在扑克博弈中的推理和游戏表现。

Comments Accepted by ICLR 2026

MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolution

MARBLE: 多智能体推理用于生物信息学学习与进化

Sunghyun Kim, Seokwoo Yun, Youngseo Yun, Youngrak Lee, Sangsoo Lim

机构 * Division of AI Convergence, Dongguk University, Seoul, South Korea（东国大学人工智能融合系） ； AI Research Team, Ar-ge Inc., Seoul, South Korea（Ar-ge公司人工智能研究团队） ； Department of Biomedical Engineering, Dongguk University, Goyang-si, Gyeonggi-do, South Korea（东国大学生物医学工程系） ； Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, South Korea（东国大学计算机科学与人工智能系） 纠错

专题命中 规划决策 ：agent(title)；multi-agent(title)；分类 cs.LG

AI总结 MARBLE通过多智能体推理实现生物信息学模型的稳定优化，提升性能并保持鲁棒性。