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您的检索式：作者名="Beishui Liao"
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| 1 | Causal Inference显示文摘Causal inference is a powerful modeling tool for explanatory analysis,which might enable current machine learning to become explainable.How to marry causal inference with machine learning to develop explainable artificial intelligence(XAI)algorithms is one of key steps toward to the artificial intelligence 2.0.With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence,we invited researchers working on causal inference to write this survey from different aspects of causal inference.This survey includes the following sections:“Estimating average treatment effect:A brief review and beyond”from Dr.Kun Kuang,“Attribution problems in counterfactual inference”from Prof.Lian Li,“The Yule–Simpson paradox and the surrogate paradox”from Prof.Zhi Geng,“Causal potential theory”from Prof.Lei Xu,“Discovering causal information from observational data”from Prof.Kun Zhang,“Formal argumentation in causal reasoning and explanation”from Profs.Beishui Liao and Huaxin Huang,“Causal inference with complex experiments”from Prof.Peng Ding,“Instrumental variables and negative controls for observational studies”from Prof.Wang Miao,and“Causal inference with interference”from Dr.Zhichao Jiang. | Kun Kuang Lian Li Zhi Geng Lei Xu Kun Zhang Beishui Liao Huaxin Huang Peng Ding Wang Miao Zhichao Jiang | 2020 | Engineering2020，6，3： | 8 |
| 2 | Dynamics of argumentation sys- tems: A division-based method 显示文摘 | Beishui Liao Li Jina Robert C | 2011 | Artificial Intelligence2011，，175： | 1 |
| 3 | A model of multi - agent system based on policies and contracts 显示文摘 | Liao Beishui Gao i | 2005 | Lecture Notes in Artificial Intelli- gence2005，，1： | 1 |
| 4 | Dynamics of argumentation systems: A division-based method显示文摘 | Beishui Liao Li Jin Robert C. Koons | 2011 | Artificial Intelligence2011，，11： | 1 |
| 5 | On Interdisciplinary Studies of a New Generation of Artificial Intelligence and Logic显示文摘A new generation of artificial intelligence(NGAI),currently based on big data and machine learning,follows a path of connectionism.Although this path achieves huge success in data-intensive applications under closed environments,there are some bottleneck problems,including a lack of explainability,the difficulty of ethical alignment,the weakness ofcognitive reasoning,etc.To address these problems inevitably involves thedepiction of information from an open,dynamic and real environment and the modeling of human reasoning and explanation mechanisms.Formal argumentation is a general formalism for modeling various types of knowledge representation and reasoning in a context of disagreement,and is flexible enough to incorporate other types of knowledge for decisionmaking,such as preferences,weights,and probabilities.Meanwhile,there are various approaches for efficient computation of argumentation semantics by exploiting the locality and modularity of argumentation,and for providing explanations based on arguments and dialogues.The organic combination of formal argumentation with existing big data and machine learning techniques can be expected to break through some existing technical bottlenecks and facilitate the sustainable development of NGAI. | Liao Beishui | 2022 | Social Sciences in China2022，43，3： | 0 |
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