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If your search tree's branching factor is finite but its depth is unbounded, then DFS isn't "complete". What that means is that it may not ever find the goal node even if it exists.
The depth-limited minimax will achieve the same output as minimax without depth-limited, but can sometimes use less memory. Why is the above answer is wrong? I mean, don't both of these algorithms always achieve the same output, and because the depth-limited minimax doesn't always explore all the states this makes it use less memory?
Why the space complexity of depth-first search is O(bm)? Hot Network Questions Does the duty to rescue in German Law (StGB §323c) only apply for accidents, or also for deliberate acts?
Iterative deepening depth-first search expands at most twice as many nodes as breadth-first search since it only needs to keep track of the current generation of nodes, and not all visited nodes. $\endgroup$ –
I know uninformed algorithms, like depth-first search and breadth-first search, do not store or maintain a list of unsearched nodes like how informed search algorithms do. But the main problem with uninformed algorithms is they might keep going deeper, theoretically to infinity if an end state is not found but they exist ways to limit the ...
Normally in minimax (or any form of depth-first search really), we do not store nodes in memory for the parts we have already searched. The tree is only implicit, it's not stored anywhere explicitly. We typically implement these algorithms in a recursive memory.
One of the more standard assumptions when first introducing new students to search algorithms (like Depth-First Search, Breadth-First Search which you've also likely heard about or will hear about soon, etc.) is indeed that our goal is to find some sort of solution, and only find one.
I believe that in the book Artificial Intelligence: A Modern Approach (which you may be reading at the moment) they introduce DFS and Breadth-First Search this way, as a first milestone before reaching more complex algorithms like A*. Now, you may be wondering why such search algorithms should be considered AI.
Breadth-First Search needs memory to remember "where it was" in all the different branches, whereas Depth-First Search completes an entire path first before recursing back -- which doesn't really require any memory other than the stack trace.
Whenever I use an odd number for depth, my algorithm works fine. But when I use an even number, the bottom node because a min node and AI looses the game. Can anyone confirm that when using MAX as the root node, the depth should always be an odd number? AFAIK, it's never said explicitly in the algorithm's description. Thanks