Search tree - Definition
Tree search algorithms are specialized versions of graph search algorithms, which take the properties of trees into account.
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Search Tree Project
By James Matthews
Create a program that allows you to create a binary tree and then search it for a given data item.
Ternary search trees are usually rejected in favor of hash tables. Also, there are other ways to construct a trie.
Search Trees
The problem of chess has been tackled extremely often in A.I., and the earliest work dates back from the period when classical A.I. used to be more popular (back in the 40s and 50s).
search tree a tree structure, either explicit or implicit, of the states or goals considered in a search process. The initial state is the root of the tree; branches from a state correspond to operator applications or subgoals.
Depth-first search tree with backtracking can be used to implement systematic generate-and-test procedure.
Alpha-beta pruning is a search algorithm that reduces the number of nodes that need to be evaluated in the search tree by the minimax algorithm. ... Look up Premise in Wiktionary, the free dictionary. ...
Data structures for search trees
data type node
components: STATE, PARENT-NODE, OPERATOR, DEPTH, PATH-COST
the state in the state space to which the node corresponds; ...
Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e.
Alpha-beta pruning can be explained simply as a technique for not exploring those branches of a search tree that analysis indicates not to be of further interest either to the player making the analysis (this is obvious) or to his opponent (and this ...
In a typical episode, one is located at a certain node A in the search tree, and examines two or more possible moves, say, B and C. Each of these is somehow evaluated to yield values V(B) and V(C).
Since for large depth d, the depth first search requires quite a large runtime, an alternative way to solve the problem is by controlling the depth of the search tree.
A big tree is much worse than two small trees, so it's better to have two small search trees. With A*, however, you don't always get a tree. You often get a path that has nearby map areas explored, but it doesn't fan out like Dijkstra's algorithm.
the secret is being able to ignore the overwhelming majority of sequences, and focus attention on those relatively few which have some real promise. But how do you tell in advance which sequences to ignore? How do you prune from the search tree ...
 See also: Branch, Artificial intelligence, Search algorithm, AI, Knowledge
  
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