Theory revision with queries:: DNF formulas

被引:7
|
作者
Goldsmith, J
Sloan, RH
Turán, G
机构
[1] Univ Kentucky, Dept Comp Sci, Lexington, KY 40506 USA
[2] Univ Illinois, Dept Comp Sci, Chicago, IL USA
[3] Natl Sci Fdn, Arlington, VA 22230 USA
[4] Univ Illinois, Dept Math Stat & Comp Sci, Chicago, IL 60680 USA
[5] Univ Szeged, Hungarian Acad Sci, Res Grp AI, Szeged, Hungary
[6] Univ Illinois, Dept Elect Engn & Comp Sci, Chicago, IL 60680 USA
[7] Boston Univ, Dept Comp Sci, Boston, MA 02215 USA
基金
美国国家科学基金会;
关键词
theory revision; query learning; computational learning theory; knowledge revision; disjunctive normal form; Boolean function learning;
D O I
10.1023/A:1013641821190
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The theory revision, or concept revision, problem is to correct a given, roughly correct concept. This problem is considered here in the model of learning with equivalence and membership queries. A revision algorithm is considered efficient if the number of queries it makes is polynomial in the revision distance between the initial theory and the target theory, and polylogarithmic in the number of variables and the size of the initial theory. The revision distance is the minimal number of syntactic revision operations, such as the deletion or addition of literals, needed to obtain the target theory from the initial theory. Efficient revision algorithms are given for three classes of disjunctive normal form expressions: monotone k-DNF, monotone m-term DNF and unate two-term DNF. A negative result shows that some monotone DNF formulas are hard to revise.
引用
收藏
页码:257 / 295
页数:39
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