## Modeling Intelligent Control of Distributed Cooperative Inferencing (1997)

Citations: | 5 - 0 self |

### BibTeX

@TECHREPORT{Williams97modelingintelligent,

author = {Edward Michael Williams and Edward Michael Williams and Major Usaf and Major Usaf and Robert A. Calico},

title = {Modeling Intelligent Control of Distributed Cooperative Inferencing},

institution = {},

year = {1997}

}

### OpenURL

### Abstract

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.1 Goals and Scope . . . . . . . . . . . . . . . . . . . . . . . . . 1-2 1.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 Anytime algorithms . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.2 Algorithm Combinations . . . . . . . . . . . . . . . . . . . . 2-3 2.3 Control Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.4 Intelligent Control . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.5 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . 2-8 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 III. Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1 Phase 1: Architecture Development . . . . . . . . . . . . . . 3-1 ...

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Citation Context ...e optimal answer is found, fewer branches of the search tree would need to be explored to confirm that the optimal answer was found. This analysis explains the mixed results of our initial experiments=-=(37)-=-; all the combinations that showed an increase in performance were combinations containing at least one exact algorithm and at least one approximate algorithm. It was also frequently the case that suc... |

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Citation Context ...e inevitable error from reality is compensated for by the continual monitoring and control activities. B.2 Genetic Algorithms (GAs) The GAs used in our experiments were simple GAs based on the Genesis=-=(12)-=- framework utilizing both mutation and crossover. We represented a solution (a complete assignment to the underlying Bayesian Network) as an array of integers; each element of the array was converted ... |

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