@article{aja:fuzzy,
    author = {Santiago Aja-Fern\'andez and Carlos Alberola-L\'opez and George V.
Cybenko},
    title = {A fuzzy {MHT} algorithm applied to text-based information tracking},
    journal = {IEEE Transactions on Fuzzy Systems},
    year = {2002},
    month = {June},
    volume = {10},
    pages = {360--374},
    publisher = {IEEE Computer Society Press},
    copyright = {IEEE},
    group = {agents,coabs},
    url = {http://agent.cs.dartmouth.edu/papers/aja:fuzzy.pdf},
    abstract = {We carry out a detailed analysis of a fuzzy version of Reid's
classical multiple hypothesis tracking (MHT) algorithm. Our fuzzy version is
based on well-known fuzzy feedback systems, but the fact that the system we
describe is specialized for likelihood discrimination makes this study
particularly novel. We discuss several techniques for rule activation. One of
them, namely the sum-product, seems particularly useful for likelihood
management and its linearity makes it tractable for further analysis. Our
analysis is performed in two stages: 1) we demonstrate that, with
appropriately chosen rules, our system can discriminate the correct
hypothesis; and 2) the steady-state behavior with a constant input is
characterized analytically. This enables us to establish the optimality of
the sum-product method and it also gives a simple procedure to predict the
system's behavior as a function of the rule base. We believe this fact can be
used to devise a simple procedure for fine-tuning the rule base according to
the system designer's needs. The application driving our fuzzy MHT
implementation and analysis is the tracking of natural language text-based
messages. This application is used as an example throughout the paper.}
}