應(yīng)用場(chǎng)景導(dǎo)讀：主體接受新信息、修正自己的信念，這是一個(gè)非常普遍的現(xiàn)象。邏輯學(xué)家們從20世紀(jì)80年代開(kāi)始研究其中的邏輯規(guī)律，建立了信念修正理論。在AGM框架中，智能數(shù)據(jù)庫(kù)不僅負(fù)責(zé)存儲(chǔ)計(jì)劃者（planner）的信念，還負(fù)責(zé)保持它們的一致性。在強(qiáng)化的框架中有兩類(lèi)數(shù)據(jù)庫(kù)，一個(gè)存儲(chǔ)信念（beliefs），一個(gè)存儲(chǔ)意圖（intentions），不僅負(fù)責(zé)維持每類(lèi)數(shù)據(jù)庫(kù)的一致性，還維持它們之間的一致性。
標(biāo)題：
反思邏輯程序上下文中的AGM式信念修正
摘要：
信念修正主要研究背景的邏輯單調(diào)性。本文中，我們研究的其實(shí)是根本邏輯非單調(diào)時(shí)的信念修正——這是一個(gè)正在探索中的有趣問(wèn)題。尤其是，我們將專(zhuān)注于回答集語(yǔ)義中被表示為邏輯程序的信念本身，而新信息也被類(lèi)似表示為一個(gè)邏輯程序。我們的方式是通過(guò)不同于單調(diào)集中的觀察，必要時(shí)維護(hù)信念的修訂主體需要拋棄一些舊信念，一個(gè)非單調(diào)集的連貫性也可以通過(guò)添加新信念恢復(fù)。我們將分別通過(guò)句法和模型-理論方法定義兩個(gè)修正函數(shù)，并用定理把它們表示描述出來(lái)了。
第一作者簡(jiǎn)介：
Zhiqiang Zhuang
澳大利亞格里菲斯大學(xué)集成智能研究所，格里菲斯大學(xué)博士后，新南威爾士大學(xué)博士，研究領(lǐng)域?yàn)橹R(shí)表示和推理。
發(fā)表論文摘選：
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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