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» Rough Sets for Uncertainty Reasoning
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AUTOMATICA
2006
103views more  AUTOMATICA 2006»
15 years 6 months ago
A universal iterative learning stabilizer for a class of MIMO systems
Design of iterative learning control (ILC) often requires some prior knowledge about a system's control matrix. In some applications, such as uncalibrated visual servoing, th...
Ping Jiang, Huadong Chen, Leon C. A. Bamforth
SUM
2007
Springer
16 years 2 days ago
Combining Uncertain Outputs from Multiple Ontology Matchers
Abstract. An ontology matching method (or a matcher) aims at matching every entity (or concept) in one ontology to the most suitable entity (or entities) in another ontology. Usual...
Ying Wang, Weiru Liu, David A. Bell
ECSQARU
2009
Springer
15 years 9 months ago
Different Representations of Fuzzy Vectors
Fuzzy vectors were introduced as a description of imprecise quantities whose uncertainty originates from vagueness, not from a probabilistic model. Support functions are a classica...
Jiuzhen Liang, Mirko Navara, Thomas Vetterlein
IJCAI
1989
15 years 7 months ago
Maximum Entropy in Nilsson's Probabilistic Logic
Nilsson's Probabilistic Logic is a set theoretic mechanism for reasoning with uncertainty. We propose a new way of looking at the probability constraints enforced by the fram...
Thomas B. Kane
ATAL
2008
Springer
15 years 8 months ago
Simultaneously modeling humans' preferences and their beliefs about others' preferences
In strategic multiagent decision making, it is often the case that a strategic reasoner must hold beliefs about other agents and use these beliefs to inform its decision making. T...
Sevan G. Ficici, Avi Pfeffer