A cost-aware Bayesian sequential decision-making strategy for domain search and object classification using a limited-range sensor is presented. On one hand, it is risky to allocate all available sensing resources at a single location while ignoring other regions. On the other hand, the sensor may miss-detect or miss-classify a critical object with insufficient observations. Therefore, we develop a decision-making strategy that balances the tolerable risks and the desired decision precision under limited resources.


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    Title :

    Cost-Aware Bayesian Sequential Decision-Making for Search and Classification


    Contributors:
    Wang, Y. (author) / Hussein, I. I. (author) / Brown, D. R. (author) / Erwin, R. S. (author)


    Publication date :

    2012-07-01


    Size :

    3061584 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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