What are the practical applications of the AO * algorithm?
Those who have worked or have worked in the field of artificial intelligence (or equivalent) should have a good knowledge of the AO * algorithm.
It's pretty clear that this is a generic algorithm.
Have any of you come across any practical application of the AO * algorithm? Some of you may have already worked on it.
So, it would be great if you could share your thoughts or experiences on the AO * algorithm, how it can actually be used in practice. What is its strength?
Those who do not know the AO * algorithm can refer to the following pdf (size -291KB)
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AO * is rarely used in practical applications. This is useful for finding game trees, solving problems, etc., but in most cases, more domain search algorithms are used instead (for example, alpha-beta pruning for game trees, general planning algorithms, or specific areas).
In particular, AI is knowledge-intensive and practical applications make extensive use of knowledge of specific areas or problem conditions to get better (faster or better solutions).
Game search is an example where full-latitude search is standard, but this may be due to the small (compared to other domains) search space. Even in the search for a game tree, specific problems are widely used, i.e. Often, the search ends only in quiescent states (ie, during a forced exchange or when there is a check).
In planning, knowledge is often used to guide the search for the generated decision space, rather than to find the state of the space. This gives suboptimal solutions, but for many areas it provides reasonable solutions at a much lower cost.
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