Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Autonomous Agents and Multi-Agent Systems
Analysis of a Fusion Method for Combining Marginal Classifiers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Integrating simulation and geographic information system
Proceedings of the 2008 Spring simulation multiconference
Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Information Sciences: an International Journal
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This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory