I recently implemented depth-first iterative deepening in an Artificial Intelligence class project to solve the classic missionaries and cannibals problem [0].

I recently implemented depth-first iterative deepening in an Artificial Intelligence class project to solve the classic missionaries and cannibals problem [0]. The professor remarked that while there have been some optimizations over the last few decades, using them can be quite messy – to the point where the combination of A* and iterative deepening is still commonly used in the field. I'm fairly certain that the claim in the introduction – "Unfortunately, current AI texts either fail to mention this algorithm or refer to it only in the context of two-person game searches" – is no longer true. From my current textbook (Artificial Intelligence: A Modern Approach [1]): "Iterative deepening search (or iterative deepening depth-first search) is a general strategy, often used in combination with depth-first tree search, that finds the best depth limit. It does this by gradually increasing the limit — first 0, then 1, then 2, and so on — until a goal is found... In general, iterative deepening is the preferred uninformed search method when the search space is large and the depth of the solution is not known." [0] http://en.wikipedia.org/wiki/Missionaries_and_cannibals_problem http://en.wikipedia.org/wiki/Missionaries_and_cannibals_prob... [1] http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597 http://www.amazon.com/Artificial-Intelligence-Modern-Approac...