By M. Tim Jones
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Additional resources for Artificial Intelligence: A Systems Approach (w/CDROM)(Computer Science) (Engineering)(AI)
11: Search order of the DFS algorithm over a small tree. NT HE C D O The space complexity for DFS is O(bd) where the time complexity is geometric (O(bd)). This can be very problematic on deep branching graphs, as the algorithm will continue to the maximum depth of the graph. If loops are present in the graph, then DFS will follow these cycles indeﬁnitely. For this reason, the DFS algorithm is not complete, as cycles can prohibit the algorithm from ﬁnding the goal. If cycles are not present in the graph, then the algorithm is complete (will always ﬁnd the goal node).
15: Bidirectional search meeting in the middle at node H. 42 Artiﬁcial Intelligence Bidirectional Search The Bidirectional Search algorithm is a derivative of BFS that operates by performing two breadth-ﬁrst searches simultaneously, one beginning from the root node and the other from the goal node. When the two searches meet in the middle, a path can be reconstructed from the root to the goal. 15). This is accomplished by keeping a closed list of the nodes visited. Bidirectional search is an interesting idea, but requires that we know the goal that we’re seeking in the graph.
UNINFORMED SEARCH The uninformed search methods offer a variety of techniques for graph search, each with its own advantages and disadvantages. These methods are explored here with discussion of their characteristics and complexities. Big-O notation will be used to compare the algorithms. This notation deﬁnes the asymptotic upper bound of the algorithm given the depth (d) of the tree and the branching factor, or the average number of branches (b) from each node. There are a number of common complexities that exist for search algorithms.