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Tree

As explained in Grokking AI Algorithms, Second Edition

A tree is a connected acyclic data structure organized into nodes with a single root and hierarchical parent-child relationships.

A tree is a connected, acyclic structure that represents a hierarchy. It has a single root node, and each child has one parent; branches may contain smaller subtrees. section 2.7

Use family relationships to read a tree:

  • The root is the topmost node. section 2.7
  • A parent connects directly to one or more children; children with the same parent are siblings. section 2.7
  • A leaf has no children, so its degree—the number of children—is zero. section 2.7
  • An edge connects two nodes, while a path is a sequence of nodes and edges. section 2.7
  • Depth measures a node’s level from the root, and height measures the tree’s total height. section 2.7

A maze as a tree

Treat the maze’s starting position as the root. Each possible move becomes a child, and repeating this for every move generates branches. A path from the root to a goal leaf records the moves needed to solve the maze. section 2.7 section 2.10

Breadth-first search explores a tree depth by depth using a FIFO queue. It records each node’s parent, checks whether a neighbor is the goal, and reconstructs the path by following parents back to the root. The first path found is returned, but an uninformed search is not guaranteed to find a more optimal path. section 2.10

Figure 2.12 The main attributes of a tree

Inspect the labeled root, parent, child, leaf, depth, height, edge, and path relationships. section 2.7

Figure 2.19 Maze movement tree generation using BFS

Trace how BFS generates movement branches while exploring a maze. section 2.10

How Grokking AI Algorithms, Second Edition covers it

How deep it goes
Defines core terminology and traces BFS traversal, but stops short of formal proofs or broader implementation trade-offs.
Examples
Uses labeled tree diagrams, a maze movement tree, BFS visit sequences, and a Python BFS listing.
What you can do after
Apply it via breadth-first search to generate branches, visit nodes by depth, and reconstruct a path to a goal.
How it connects
Connects trees with graphs, breadth-first search, nodes, paths, traversal, and maze solving.

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This explainer was generated by Manning's knowledge graph from the text of Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.