Application scenario reading: The subject accepts new information and corrects his beliefs. This is a very common phenomenon. Logicians began to study the laws of logic in the 1980s and established belief revision theory. In the AGM framework, smart databases are responsible not only for the planner's beliefs, but also for maintaining their consistency. In the enhanced framework there are two types of databases, one for beliefs and one for intentions, not only maintaining the consistency of each type of database, but also maintaining their consistency.
title:
Rethink AGM-style belief correction in the context of logic programs
Summary:
Belief revision mainly focuses on the logical monotony of the background. In this paper, what we are studying is actually the belief that the fundamental logic is nonmonotonic – this is an interesting issue that is being explored. In particular, we will focus on the beliefs in the set of semantics that are represented as logic programs, and the new information is similarly represented as a logic program. Our approach is to adopt observations that are different from monotonous focus, where the main body of revision needed to preserve beliefs needs to abandon some old beliefs, and the consistency of a non-monotonic set can also be restored by adding new beliefs. We will define two corrective functions by syntax and model-theoretic methods, respectively, and use the theorem to describe them.
The first author introduction:
Zhiqiang Zhuang
Institute of Integrated Intelligence, Griffith University, Australia, post-doctoral fellow at Griffith University, Ph.D., University of New South Wales, research areas for knowledge representation and reasoning.
Published papers selected:
2016Zhiqiang 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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