Pursuit and evasion tends to be incorporated in human nature from a very long time with a very huge range of activities. Here, we are going to create an intelligent chase algorithm, which uses two basic approaches. After which we will use the newly created equations to simulate both approaches and provide graphical results. This analysis is based upon the fact that in modern days we can estimate the speed of a moving object and then chase it down depending upon its speed. We will also be taking the accuracy of such estimation in picture and depending upon a certain accuracy and other inputs the simulation will provide the result and performance of both the approaches.
By adapting a previously written percolation model in C, the threshold probabilities for square, triangular, and cubic lattice types were confirmed. An algorithm to count the distribution of cluster sizes at a variety of percolation probabilities was developed, and the expected trends towards the so called infinite cluster was achieved. An equivalent bond percolation model was adapted to the original site algorithm, and by treating occupied bonds as springs, a total compression trend for the model was constructed, which implied that structures under the boundary conditions that were imposed does not have behavior that changes the total compression constant significantly at the percolation threshold.\\
We are given spans of the target text which align to concepts in the AMR graph.These alignment do not cover every token in the target sentnce. Typically function words are not aligned to any graph fragment. Next, we obtain word alignments between the target sentence and source sentence. Since we have word alignments between target and source, and phrase alignments between target and AMR graph, we must convert the word alingments into phrase alignments. The phrases on the source side will then be projected to the AMR concepts via the target sentence