کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
376858 658327 2015 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Belief revision in Horn theories
ترجمه فارسی عنوان
تجدید نظر در نظریه های شاخ
کلمات کلیدی
نمایندگی دانش، بازنگری اعتقاد، منطق شاخ
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

This paper investigates belief revision where the underlying logic is that governing Horn clauses. We show that classical (AGM) belief revision doesn't immediately generalise to the Horn case. In particular, a standard construction based on a total preorder over possible worlds may violate the accepted (AGM) postulates. Conversely, in the obvious extension to the AGM approach, Horn revision functions are not captured by total preorders over possible worlds. We address these difficulties by introducing two modifications to the AGM approach. First, the semantic construction is restricted to “well behaved” orderings, what we call Horn compliant orderings. Second, the revision postulates are augmented by an additional postulate. Both restrictions are redundant in the AGM approach, but not in the Horn case. In a representation result we show that the class of revision functions captured by Horn compliant total preorders over possible worlds is precisely that given by the (extended) set of Horn revision postulates. Further, we show that Horn revision is compatible with work in iterated revision and work concerning relevance in revision. We also consider specific revision operators. Arguably this work is interesting for several reasons. It extends AGM revision to inferentially-weaker Horn theories; hence it sheds light on the theoretical underpinnings of belief change, as well as generalising the AGM paradigm. Thus, this work is relevant to revision in areas that employ Horn clauses, such as deductive databases and logic programming, as well as areas in which inference is weaker than classical logic, such as in description logic.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence - Volume 218, January 2015, Pages 1–22
نویسندگان
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