Bayesian-Based Search Decision Framework and Search Strategy Analysis in Probabilistic Search

<div class="floats-full-page floats-full-page-other"><h2>Algorithm 1</h2><div class="floats-full-page-caption"><div> The searching process of the myopic strategy.</div></div><div class="floats-full-page-content"><table class="algorithm-group"><tr><td><table class="algorithm" id="alg1"><tr><td>(1)</td><td>Initialize the belief map M</td></tr><tr><td>(2)</td><td>Initialization decision threshold <i>B</i>, <i>B</i> and time <i>t</i> = 0</td></tr><tr><td>(3)</td><td>Calculate the aggregate belief <i>B</i>(0)</td></tr><tr><td>(4)</td><td>while <i>B</i> &lt; <i>B</i>(<i>t</i>) &lt; <i>B</i> do</td></tr><tr><td>(5)</td><td> Calculate the cell <i>a</i><sub>th+1</sub> that should be detected at the next moment according to the myopic strategy</td></tr><tr><td>(6)</td><td> Check the cell <i>a</i><sub>th</sub> and get detection result <i>D</i><sub><i>a</i></sub>(<i>t</i>)</td></tr><tr><td>(7)</td><td> Update <i>M</i> based on <i>D</i><sub><i>a</i></sub>(<i>t</i>)</td></tr><tr><td>(8)</td><td> Calculate <i>B</i>(<i>t</i>)</td></tr><tr><td>(9)</td><td> <i>t</i> = <i>t</i> + 1</td></tr><tr><td>(10)</td><td>end while</td></tr><tr><td>(11)</td><td>return Search result</td></tr></table></td></tr></table></div></div>

Algorithm 1 | Bayesian-Based Search Decision Framework and Search Strategy Analysis in Probabilistic Search

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Algorithm 1: Bayesian-Based Search Decision Framework and Search Strategy Analysis in Probabilistic Search

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Algorithm 1

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