应用场景导读：主体接受新信息、修正自己的信念，这是一个非常普遍的现象。逻辑学家们从20世纪80年代开始研究其中的逻辑规律，建立了信念修正理论。在AGM框架中，智能数据库不仅负责存储计划者（planner）的信念，还负责保持它们的一致性。在强化的框架中有两类数据库，一个存储信念（beliefs），一个存储意图（intentions），不仅负责维持每类数据库的一致性，还维持它们之间的一致性。
标题：
反思逻辑程序上下文中的AGM式信念修正
摘要：
信念修正主要研究背景的逻辑单调性。本文中，我们研究的其实是根本逻辑非单调时的信念修正——这是一个正在探索中的有趣问题。尤其是，我们将专注于回答集语义中被表示为逻辑程序的信念本身，而新信息也被类似表示为一个逻辑程序。我们的方式是通过不同于单调集中的观察，必要时维护信念的修订主体需要抛弃一些旧信念，一个非单调集的连贯性也可以通过添加新信念恢复。我们将分别通过句法和模型-理论方法定义两个修正函数，并用定理把它们表示描述出来了。
第一作者简介：
Zhiqiang Zhuang
澳大利亚格里菲斯大学集成智能研究所，格里菲斯大学博士后，新南威尔士大学博士，研究领域为知识表示和推理。
发表论文摘选：
2016
Zhiqiang Zhuang, Zhe Wang, Kewen Wang, Guilin Qi, DL-Lite Contraction and Revision, Journal of Artificial Intelligence Research 56 (2016) 329-378.
Zhiqiang Zhuang, Maurice Pagnucco, Yan Zhang, Inter-definability of Horn Contraction and Revision, Accepted for publication at Journal of Philosophical Logic.
Zhiqiang Zhuang, James Delgrande, Abhaya Nayak, Abdul Sattar, Reconsidering AGM-Style Belief Revision in the Context of Logic Programs, To appear in proceedings of the 22nd European Conference on Artificial Intelligence (ECAI-16).
2015
Zhiqiang Zhuang, Zhe Wang, Kewen Wang, James Delgrande, Extending AGM Contraction to Arbitrary Logics, In proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15), pages 3299-3307.
Kinzang Chhogyal, Abhaya Nayak, Zhiqiang Zhuang, Abdul Sattar, Probabilistic Belief Contraction Using Argumentation, In proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15), pages 2854-2860.
Yisong Wang, Kewen Wang, Zhe Wang, Zhiqiang Zhuang, Knowledge Forgetting in Circumscription: A Preliminary Report, In proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 1649-1655.
Sebastian Binnewies, Zhiqiang Zhuang, Kewen Wang, Partial Meet Revision and Contraction in Logic Programs, In proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 1439-1445.
Guilin Qi, Zhe Wang, Kewen Wang, Xuefeng Fu, Zhiqiang Zhuang, Approximating Model-based ABox Revision in DL-Lite: Theory and Practice, In proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 254-260.
Zhe Wang, Kewen Wang, Zhiqiang Zhuang, Guilin Qi, Instance-driven Ontology Evolution in DL-Lite, In proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 1656-1662.
2014
Zhiqiang Zhuang, Maurice Pagnucco, Entrenchment-Based Horn Contraction, Journal of Artificial Intelligence Research (JAIR) 51 (2014), pages 227-254.
Zhiqiang Zhuang, Zhe Wang, Kewen Wang, Guilin Qi, Contraction and Revision over DL-Lite TBoxes, In proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14), pages 1149-1156.
2013
Yisong Wang, Zhiqiang Zhuang, Kewen Wang, Belief Change in Nonmonotonic Multi-Context Systems, In proceedings of the 12th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR-13), pages 543-555.
Zhiqiang Zhuang, Maurice Pagnucco, Yan Zhang, Definability of Horn Revision from Horn Contraction. In proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pages 1205-1211.
2012
Zhiqiang Zhuang, Maurice Pagnucco, Model Based Horn Contraction. In Proc. of the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR-12), pages 169-178.
2011
Zhiqiang Zhuang, Maurice Pagnucco, Transitively Relational Partial Meet Horn Contractions. In proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), pages 1132-1138.
2010
Zhiqiang Zhuang, Maurice Pagnucco, Two Methods for Constructing Horn Contractions. In proceedings of the 23rd Australasian Conference on Artificial Intelligence 2010 (AI-10), pages 72-81.
Zhiqiang Zhuang, Maurice Pagnucco, Horn Contraction via Epistemic Entrenchment. In proceedings of the 12th European Conference on Logics in Artificial Intelligence (JELIA-10), pages 339-351.
2007
Zhiqiang Zhuang, Maurice Pagnucco, and Thomas Meyer, Implementing Iterated Belief Change Via Prime Implicates. In proceedings of the 20th Australian Joint Conference on Artificial Intelligence (AI-07), pages 507-518. via PRICAI 2016
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