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


A cost-aware Bayesian sequential decision-creating strategy for domain search and object classification using a limited-vary sensor is presented. On one hand, it's risky to allocate all accessible sensing resources at a single location whereas ignoring other regions. On the other hand, the sensor might miss-detect or miss-classify a crucial object with insufficient observations. Thus, we tend to develop a call-creating strategy that balances the tolerable risks and the desired call precision underneath restricted resources.

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