Zongzhang Zhang @ NJU-AI

Full Publication List

2023

• Chenyang Wu and Zongzhang Zhang. Surfing Information: The Challenge of Intelligent Decision-Making. Intelligent Computing, 2023, 2: Article 0041. [PDF]
• Jiacheng Xu, Chao Chen, Fuxiang Zhang, Lei Yuan, Zongzhang Zhang, and Yang Yu. Internal Logical Induction for Pixel-Symbolic Reinforcement Learning. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2023), pages 2825–2837, Long Beach, CA, USA, 2023. [PDF]
• Yuhang Ran, Yi-Chen Li, Fuxiang Zhang, Zongzhang Zhang, and Yang Yu. Policy Regularization with Dataset Constraint for Offline Reinforcement Learning. In: Proceedings of the 40th International Conference on Machine Learning (ICML-2023), pages 28701-28717, Honolulu, Hawaii, USA, 2023. [PDF + Supplementary] [Code]
• Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, and Tie-Yan Liu. Retrosynthetic Planning with Dual Value Networks. In: Proceedings of the 40th International Conference on Machine Learning (ICML-2023), pages 22266-22276, Honolulu, Hawaii, USA, 2023. [PDF + Supplementary]
• Fuxiang Zhang, Chengxing Jia, Yi-Chen Li, Lei Yuan, Yang Yu, and Zongzhang Zhang. Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data. In: Proceedings of the 11th International Conference on Learning Representations (ICLR-2023), Kigali, Rwanda, 2023. [PDF + Supplementary] [Code]
• Weijian Liao, Zongzhang Zhang, and Yang Yu. Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 8746-8754, Washington, DC, USA, 2023. [PDF]
• Aoran Wang, Hongyang Yang, Feng Mao, Zongzhang Zhang, Yang Yu, and Xiaoyang Liu. Anti-Drifting Feature Selection via Deep Reinforcement Learning (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 16356-16357, Washington, DC, USA, 2023. [PDF]     (Best Student Abstract Award - Honorable Mention)
• Chao Chen, Dawei Wang, Feng Mao, Zongzhang Zhang, and Yang Yu. Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 16180-16181, Washington, DC, USA, 2023. [PDF]
• Feng Chen, Chenghe Wang, Fuxiang Zhang, Hao Ding, Qiaoyong Zhong, Shiliang Pu, and Zongzhang Zhang. Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 16182-16183, Washington, DC, USA, 2023. [PDF]
• Fuguang Han and Zongzhang Zhang. Expert Data Augmentation in Imitation Learning (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 16220-16221, Washington, DC, USA, 2023. [PDF]
• Yi-Chen Li, Wen-Jie Shen, Boyu Zhang, Feng Mao, Zongzhang Zhang, and Yang Yu. Learning Generalizable Batch Active Learning Strategies via Deep Q-Networks (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 16258-16259, Washington, DC, USA, 2023. [PDF]
• Renzhe Zhou, Zongzhang Zhang, and Yang Yu. Model-based Offline Weighted Policy Optimization (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 16392-16393, Washington, DC, USA, 2023. [PDF]
• Xuhui Liu, Feng Xu, Xinyu Zhang, Tianyuan Liu, Shengyi Jiang, Ruifeng Chen, Zongzhang Zhang, and Yang Yu. How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement. In: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2023), pages 1276-1284, London, United Kingdom, 2023. [PDF] [Supplementary] [Code]
• Lei Yuan, Tao Jiang, Lihe Li, Feng Chen, Zongzhang Zhang, and Yang Yu. Robust Cooperative Multi-agent Reinforcement Learning via Multi-view Message Certification. SCIENCE CHINA Information Sciences, 2023. [Online]
• Rongjun Qin, Feng Chen, Tonghan Wang, Lei Yuan, Xiaoran Wu, Yipeng Kang, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. Multi-Agent Policy Transfer via Task Relationship Modeling. SCIENCE CHINA Information Sciences, 2023. [Online]
• Lei Yuan, Feng Chen, Zongzhang Zhang, and Yang Yu. Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation. Frontiers of Computer Science, 2023. [Online]
• Chengxing Jia, Fuxiang Zhang, Tian Xu, Jing-Cheng Pang, Zongzhang Zhang, and Yang Yu. Model Gradient: Unified Model and Policy Learning in Model-based Reinforcement Learning. Frontiers of Computer Science, 2023. [Online]

2022

• Chenyang Wu, Tianci Li, Zongzhang Zhang, and Yang Yu. Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning. In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), pages 14210-14223, New Orleans, USA, 2022. [PDF] [Supplementary]
• Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, and Yang Yu. Efficient Multi-agent Communication via Self-supervised Information Aggregation. In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), pages 1020-1033, New Orleans, USA, 2022. [PDF] [Supplementary] [Code] [Demo]
• Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, and Yang Yu. Multi-agent Dynamic Algorithm Configuration. In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), pages 20147-20161, New Orleans, USA, 2022. [PDF] [Supplementary] [Code] [Demo]
• Rongjun Qin, Feng Chen, Tonghan Wang, Lei Yuan, Xiaoran Wu, Yipeng Kang, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. Multi-Agent Policy Transfer via Task Relationship Modeling. In: Deep RL Workshop on Neural Information Processing Systems, New Orleans, USA, 2022. [PDF]
• Di Xue, Lei Yuan, Zongzhang Zhang, and Yang Yu. Efficient Multi-Agent Communication via Shapley Message Value. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), pages 578-584, Vienna, Austria, 2022. [PDF] [Code] [Demo]
• Lei Yuan, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. Multi-Agent Concentrative Coordination with Decentralized Task Representation. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), pages 599-605, Vienna, Austria, 2022. [PDF] [Code] [Demo]
• Lei Yuan, Jianhao Wang, Fuxiang Zhang, Chenghe Wang, Zongzhang Zhang, Yang Yu, and Chongjie Zhang. Multi-Agent Incentive Communication via Decentralized Teammate Modeling. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), pages 9466-9474, Virtual Conference, 2022. [PDF] [Code] [Demo]
• Fan-Ming Luo, Shengyi Jiang, Yang Yu, Zongzhang Zhang, and Yi-Feng Zhang. Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), pages 7637-7646, Virtual Conference, 2022. [PDF] [Code]
• 常田, 章宗长, 俞扬. 随机集成策略迁移. 计算机科学与探索, 2022, 16(11): 2531-2536. [PDF]
• 章宗长, 俞扬. 单智能体强化学习. 分布式人工智能, 安波, 高阳, 俞扬等著, 电子工业出版社, 2022.11, 207-246.

2021

• Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, and Jianye Hao. Adaptive Online Packing-guided Search for POMDPs. In: Advances in Neural Information Processing Systems 34 (NeurIPS-2021), pages 28419-28430, Virtual Conference, 2021. [PDF] [Supplementary] [Code]
• Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, and Yang Yu. Cross-Modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS-2021), pages 12520-12532, Virtual Conference, 2021. [PDF] [Supplementary] [Code]
• Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, Tianpei Yang, Yanran Li, and Changjie Fan. Efficient Policy Detecting and Reusing for Non-Stationarity in Markov Games. Autonomous Agents and Multi-Agent Systems, 2021, 35(2): 1-29. [PDF]
• Feng Xu, Shengyi Jiang, Hao Yin, Zongzhang Zhang, Yang Yu, Ming Li, Dong Li, and Wulong Liu. Enhancing Context-Based Meta-Reinforcement Learning Algorithms via An Efficient Task Encoder (Student Abstract). In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-2021), pages 15937-15938, Virtual Conference, 2021. [PDF]
• Chenyang Wu, Rui Kong, Guoyu Yang, Xianghan Kong, Zongzhang Zhang, Yang Yu, Dong Li, and Wulong Liu. LB-DESPOT: Efficient Online POMDP Planning Considering Lower Bound in Action Selection (Student Abstract). In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-2021), pages 15927-15928, Virtual Conference, 2021. [PDF]
• 陈子璇, 章宗长, 潘致远, 张琳婧. 一种基于广义异步值迭代的规划网络模型. 软件学报, 2021, 32(11): 3496-3511. [PDF]
• 姜冲, 章宗长, 陈子璇, 朱佳成, 蒋俊鹏. 一种数据高效的第三人称模仿学习方法. 计算机科学, 2021, 48(2): 238-244. [PDF]

2020

• Cong Fei, Bing Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang, Xuewu Ji, and Wulong Liu. Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), pages 2929-2935, Yokohama, Japan, 2020. [PDF]
  
• Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, Weixun Wang, Yujing Hu, Yingfeng Chen, Changjie Fan, Wulong Liu, Zhaodong Wang, and Jiajie Peng. Efficient Deep Reinforcement Learning via Adaptive Policy Transfer. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), pages 3094-3100, Yokohama, Japan, 2020. [PDF]
  This work also appeared in: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2020), pages 2053-2055, as an extended abstract.
• Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, and Xiaotian Hao. Efficient Multiagent Policy Optimization Based on Weighted Estimators in Stochastic Environments. Journal of Computer Science and Technology, 2020, 35(2): 268-280. [PDF]
• Chong Jiang, Zongzhang Zhang, Zixuan Chen, Jiacheng Zhu, and Junpeng Jiang. Third-person Imitation Learning via Image Difference and Variational Discriminator Bottleneck (Student Abstract). In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-2020), pages 13819-13820, New York, USA, 2020. [PDF]
• Jiacheng Zhu, Jiahao Lin, Meng Wang, Yingfeng Chen, Changjie Fan, Chong Jiang, and Zongzhang Zhang. Generative Adversarial Imitation learning from Failed Experiences (Student Abstract). In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-2020), pages 13997-13998, New York, USA, 2020. [PDF]
• Linjing Zhang and Zongzhang Zhang. Double Replay Buffers with Restricted Gradient. In: Proceedings of the 27th International Conference on Neural Information Processing (ICONIP-2020), pages 295-306, Bangkok, Thailand, 2020. [PDF]
• Zhen Wu, Zongzhang Zhang, and Xiaofang Zhang. Recency-Weighted Acceleration for Continuous Control through Deep Reinforcement Learning. In: Proceedings of the 27th International Conference on Neural Information Processing (ICONIP-2020), pages 604-615, Bangkok, Thailand, 2020. [PDF]
• 林嘉豪, 章宗长, 姜冲, 郝建业. 基于生成对抗网络的模仿学习综述. 计算机学报, 2020, 43(2): 326-351. [PDF]

2019

• Xiaobai Ma, Katherine R. Driggs-Campbell, Zongzhang Zhang, and Mykel J. Kochenderfer. Monte-Carlo Tree Search for Policy Optimization. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019), pages 3116-3122, Macao, China, 2019. [PDF]
• Linjing Zhang, Zongzhang Zhang, Zhiyuan Pan, Yingfeng Chen, Jiangcheng Zhu, Zhaorong Wang, Meng Wang, and Changjie Fan. A Framework of Dual Replay Buffer: Balancing Forgetting and Generalization in Reinforcement Learning. In: Proceedings of the 2nd Workshop on Scaling Up Reinforcement Learning (SURL), International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2019. [PDF]
• Shan Zhong, Quan Liu, Zongzhang Zhang, and Qiming Fu. Efficient Reinforcement Learning in Continuous State and Action Spaces with Dyna and Policy Approximation, Frontiers of Computer Science, 2019, 13(1): 106-126. [PDF]
• Zixuan Chen and Zongzhang Zhang. Deep Recurrent Policy Networks for Planning under Partial Observability. In: Proceedings of the 28th International Conference on Artificial Neural Networks (ICANN-2019), Part I: Theoretical Neural Computation, pages 598-610, Munich, Germany, 2019. [PDF]
• Yisheng Wang and Zongzhang Zhang. Experience Selection in Multi-Agent Deep Reinforcement Learning. In: Proceedings of the 31st International Conference on Tools with Artificial Intelligence (ICTAI-2019), pages 864-870, Portland, USA, 2019. [PDF]
• 徐进, 刘全, 章宗长, 梁斌, 周倩. 基于多重门限机制的异步深度强化学习. 计算机学报, 2019, 42(3): 636-653. [PDF]
• 陈松, 章晓芳, 章宗长, 刘全, 吴金金, 闫岩. 基于线性动态跳帧的深度双Q网络. 计算机学报, 2019, 42(11): 2561-2573. [PDF]
• 章宗长, 王艺深. 以开源项目为驱动的软件工程课程改革与研究. 计算机教育, 2019, 289(1): 84-87. [PDF]
• 刘全, 傅启明, 章宗长译. 基于函数逼近的强化学习与动态规划. 人民邮电出版社, 2019.4, 1-249.
• 章宗长, 郝建业, 俞扬. 强化学习. 数据智能研究前沿, 徐宗本, 姚新编著, 上海交通大学出版社, 2019.12, 209-278.

2018

• Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, and Changjie Fan. A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents. In: Advances in Neural Information Processing Systems 31 (NeurIPS-2018), pages 960-970, Montreal, Canada, 2018. [PDF] [Supplementary]
• Zongzhang Zhang and Mykel J. Kochenderfer. Decision-Theoretic Planning in Partially Observable Environments. Interactions in Multiagent Systems, J. Hao and H. Leung Eds., World Scientific, 2018, 65-90. [PDF]
• Jiahao Lin and Zongzhang Zhang. ACGAIL: Imitation Learning about Multiple Intentions with Auxiliary Classifier GANs. In: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2018), pages 321-334, Nanjing, China, 2018. [PDF]
• Yan Zheng, Zhaopeng Meng, Jianye Hao, and Zongzhang Zhang. Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments. In: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2018), pages 421-429, Nanjing, China, 2018. [PDF]
• Zhiyuan Pan, Zongzhang Zhang, and Zixuan Chen. Asynchronous Value Iteration Network. In: Proceedings of the 25th International Conference on Neural Information Processing (ICONIP-2018), pages 169-180, Siem Reap, Cambodia, 2018. [PDF]
• Hongyao Tang, Jianye Hao, Tangjie Lv, Yingfeng Chen, Zongzhang Zhang, Hangtian Jia, Chunxu Ren, Yan Zheng, Changjie Fan, and Li Wang. Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction. arXiv preprint arXiv: 1809.09332v2. [PDF]
• 刘全, 翟建伟, 章宗长, 钟珊, 周倩, 章鹏, 徐进. 深度强化学习综述. 计算机学报, 2018, 41(1): 1-27. [PDF]    (2017-2021's Best Paper Award)
• 章宗长. 部分可观察环境中的序贯决策理论及方法研究. 多智能体系统及应用(卷二), 王崇骏, 史忠植, 常亮, 王文剑主编, 清华大学出版社, 2018.1, 94-111.
• 章宗长, 王艺深等译. Python数据科学导论：概念、技术与应用. 机械工业出版社, 2018.8, 1-182.

2017

• Zongzhang Zhang, Zhiyuan Pan, and Mykel J. Kochenderfer. Weighted Double Q-learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-2017), pages 3455-3461, Melbourne, Australia, 2017. [PDF]
• 刘全, 翟建伟, 钟珊, 章宗长, 周倩, 章鹏. 一种基于视觉注意力机制的深度循环Q网络模型. 计算机学报, 2017, 40(6): 1353-1366. [PDF]
• 高阳, 操龙兵, 靳小龙, 史颖欢, 章宗长, 钱宇华. 数据科学中的机器学习基础和进展. CCF2016-2017中国计算机科学技术发展报告会, 2017, 116-142. [LINK]

2016

• Zongzhang Zhang, Qiming Fu, Xiaofang Zhang, and Quan Liu. Reasoning and Predicting POMDP Planning Complexity via Covering Numbers. Frontiers of Computer Science, 2016, 10(4): 726-740. [PDF]
• Zongzhang Zhang and Quan Liu. Covering Number: Analyses for Approximate Continuous-state POMDP Planning. In: Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2016), pages 1293-1294, Singapore, Singapore, 2016. [PDF]
• Jianwei Zhai, Quan Liu, Zongzhang Zhang, Shan Zhong, Haijun Zhu, Peng Zhang, and Cijia Sun. Deep Q-learning with Prioritized Sampling. In: Proceedings of the 23rd International Conference on Neural Information Processing (ICONIP-2016), pages 13-22, Kyoto, Japan, 2016. [PDF]     (Finalist of Best Student Paper Award)
• Weisheng Qian, Quan Liu, Zongzhang Zhang, Zhiyuan Pan, and Shan Zhong. Policy Graph Pruning and Optimization in Monte Carlo Value Iteration for Continuous-State POMDPs. In: Proceedings of the 2016 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL-2016), Athens, Greece, 2016. [PDF]
• 章晓芳, 章宗长, 谢晓园, 周谊成. 一种基于优先级的迭代划分测试方法. 计算机学报, 2016, 39(11): 2307-2323. [PDF]

2015

• Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, and Aijun Bai. PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces. In: Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS-2015), pages 249-257, Jerusalem. Israel, 2015. [PDF]
  This work also appeared in: Proceedings of the 8th Annual Symposium on Combinatorial Search (SoCS-2015), pages 238-239, as a two-page abstract.
• Yicheng Zhou, Quan Liu, Qiming Fu, and Zongzhang Zhang. Trajectory Sampling Value Iteration: Improved Dyna Search for MDPs. In: Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2015), pages 1685-1686, Istanbul, Turkey, 2015. [PDF]
• Shuhua You, Quan Liu, Zongzhang Zhang, Hui Wang, and Xiaofang Zhang. Intelligent Model Learning Based on Variance for Bayesian Reinforcement Learning. In: Proceedings of the 27th International IEEE Conference on Tools with Artificial Intelligence (ICTAI-2015), pages 170-177, Salerno, Italy, 2015. [PDF]
• 钟珊, 刘全, 傅启明, 章宗长, 朱斐, 龚声蓉. 一种近似模型表示的启发式Dyna优化算法. 计算机研究与发展, 2015, 52(12): 2764-2775. [PDF]

2014

• Zongzhang Zhang, David Hsu, and Wee Sun Lee. Covering Number for Efficient Heuristic-Based POMDP Planning. In: Proceedings of the 31st International Conference on Machine Learning (ICML-2014), pages 28-36, Beijing, China, 2014. [PDF] [Supplementary]
• Aijun Bai, Feng Wu, Zongzhang Zhang, and Xiaoping Chen. Thompson Sampling based Monte-Carlo Planning in POMDPs. In: Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS-2014), pages 28-36, Portsmouth, USA, 2014. [PDF]

2013

• 章宗长, 陈小平. 杂合启发式在线POMDP规划. 软件学报, 2013, 24(7): 1589-1600. [PDF]

2012

• Zongzhang Zhang, Michael L. Littman, and Xiaoping Chen. Covering Number as a Complexity Measure for POMDP Planning and Learning. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-2012), pages 1853-1859, Toronto, Ontario, Canada, 2012. [PDF]
• Zongzhang Zhang and Xiaoping Chen. FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs. In: Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-2012), pages 934-943, Catalina Island, United States, 2012. [PDF]
• 章宗长. 部分可观察马氏决策过程的复杂性理论及规划算法研究. 博士学位论文, 中国科学技术大学, 2012/6/5. [PDF]

2010

• Zongzhang Zhang and Xiaoping Chen. Accelerating Point-Based POMDP Algorithms via Greedy Strategies. In: Proceedings of International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR-2010), pages 545-556, Darmstadt, Germany, 2010. [PDF]