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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
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Portfolio item number 2
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publications
Tensor optimization with group lasso for multi-agent predictive state representation
Published in Knowledge-Based Systems, 2021
This paper proposes a tensor optimization approach to learn a multi-agent PSR model, addressing the challenges of limited samples and increasing number of agents, and demonstrating promising performance across multiple problem domains.
Recommended citation: Biyang Ma, Jing Tang, Bilian Chen, Yinghui Pan, Yifeng Zeng (2021). " Tensor optimization with group lasso for multi-agent predictive state representation." Knowledge-Based Systems, 2021, 106893., 2021. 211(7).
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ATPT: Automate Typhoon Contingency Plan Generation from Text.
Published in AAMAS, 2021
We present and implement a framework that utilizes deep learning techniques to automate the generation of a planning domain model from natural language input, demonstrating its application in automatically generating typhoon contingency plans from official documents.
Recommended citation: Yifeng Zeng, Zhangrui Yao, Yinghui Pan, Wanqing Chen, Junxin Zhou, Junhan Chen, Biyang Ma, and Zhong Ming. (2021). "ATPT: Automate Typhoon Contingency Plan Generation from Text." In Proc. Of AAMAS ’21. 2021, 1788–1790.
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Ev-IDID: Enhancing solutions to interactive dynamic influence diagrams through evolutionary algorithms
Published in AAMAS, 2022
This demo introduces an interactive I-DID system that incorporates state-of-the-art and novel evolutionary algorithms, enabling users to specify parameters, visualize solutions, and automate behavioral model generation for multiagent sequential decision-making under uncertainty.
Recommended citation: Biyang Ma, Yinghui Pan, Yifeng Zeng, Zhong Ming.(2022). "Ev-IDID: Enhancing solutions to interactive dynamic influence diagrams through evolutionary algorithms " In Proc. Of AAMAS ’22. 2022, 1911–1913.
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LBfT: Learning Bayesian Network Structures from Text in Autonomous Typhoon Response Systems
Published in AAMAS, 2022
We demonstrate a deep learning framework that identifies typhoon-relevant variables and builds their causal relations from text, enhancing decision models in autonomous typhoon response systems using the CausalBank dataset and user domain knowledge.
Recommended citation: Yinghui Pan, Junhan Chen, Yifeng Zeng, Zhangrui Yao, Qianwen Li, Biyang Ma, Yi Ji, and Zhong Ming. (2022). "LBfT: Learning Bayesian Network Structures from Text in Autonomous Typhoon Response Systems." In Proc. Of AAMAS ’22. 2022, 1914–1916.
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Tensor decomposition for multi-agent predictive state representation
Published in Expert Systems with Applications, 2022
This paper proposes tensor techniques to learn a multi-agent PSR model, addressing the challenges of increasing agent numbers and problem complexity, and demonstrating effectiveness across multiple domains.
Recommended citation: Biyang Ma, Bilian Chen, Yifeng Zeng*, Jing Tang, Langcai Cao (2022). " Tensor decomposition for multi-agent predictive state representation." Expert Systems with Applications, 2022,115969, 2022;. 189(1).
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Improvement and Evaluation of the Policy Legibility in Reinforcement Learning
Published in AAMAS, 2023
In this article, we propose a novel reward shaping mechanism to enhance the legibility of reinforcement learning policies for intelligent agents, and develop an interactive system to gather user evaluations and demonstrate the approach’s performance.
Recommended citation: Yanyu Liu, Yifeng Zeng, Biyang Ma, Yinghui Pan, Huifan Gao, Xiaohan Huang (2023). "Improvement and Evaluation of the Policy Legibility in Reinforcement Learning." In Proc. Of AAMAS ’23. 2023, 3044–3046.
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Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks
Published in IEEE Transactions on Network Science and Engineering, 2023
This paper presents a multi-agent transfer reinforcement learning approach for efficient resource management in underwater acoustic communication networks, aiming to improve network performance and adaptability.
Recommended citation: Wang, Hui; Wu, Hongrun; Chen, Yingpin; Ma, Biyang. (2023). "Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks." IEEE Transactions on Network Science and Engineering.11(2), 2012-2023.
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Multi-population differential evolution approach for feature selection with mutual information ranking
Published in Expert Systems with Applications, 2025
This paper proposes a novel multi-population differential evolution approach for feature selection with mutual information ranking, which significantly enhances classification performance by reducing feature dimensionality and improving algorithm optimization capabilities.
Recommended citation: Fei Yu, Jian Guan, Hongrun Wu, Hui Wang, Biyang Ma. (2024). "Multi-population differential evolution approach for feature selection with mutual information ranking." Expert Systems with Applications, 260, 125404.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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