Random Minimax Game Tree Test Case Generator

Description

Generate a bounded complete fixed-branching game tree with seeded integer leaf utilities, retain preorder node IDs and portable tree JSON, and delegate alternating minimax alpha–beta evaluation; pruning counts are skipped immediate child branches, not skipped leaves or a game-playing guarantee.

A graph uses vertices and edges to represent relationships. Direction, duplicate handling, indexing, and traversal order affect its interpretation; returning a constructed graph together with a witness or calculated answer helps verify an algorithm.

When to use this tool

Use this generator to create reproducible minimax game tree test case cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply branchingFactor, depth, minimumLeafValue, maximumLeafValue, maximizing in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read rootId, nodes, leafCount, treeJson, result. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Build a complete fixed-branching game tree to the requested depth, draw integer leaf payoffs, and delegate alpha-beta search with the selected maximizing convention. Return preorder IDs, nodes, serialized tree, and search result.^[1]

Delegated operations used for this result: alpha beta. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
branchingFactor	Branching Factor	Required. Safe integer; minimum 2; maximum 6.
depth	Depth	Required. Safe integer; minimum 0; maximum 8.
minimumLeafValue	Minimum Leaf Value	Required. Safe integer.
maximumLeafValue	Maximum Leaf Value	Required. Safe integer.
maximizing	Maximizing	Required. Boolean.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
rootId	Root Id	Required. Safe integer; exactly 0.
nodes	Nodes	Required. Array of Object; no unknown keys. or Object; no unknown keys.; minimum items 1; maximum items 1023.
leafCount	Leaf Count	Required. Safe integer; minimum 1; maximum 512.
treeJson	Tree Json	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 65536.
result	Result	Required. Object; no unknown keys.
result.bestValue	Best Value	Required. Safe integer.
result.evaluatedLeaves	Evaluated Leaves	Required. Safe integer; minimum 1; maximum 512.
result.prunedBranches	Pruned Branches	Required. Safe integer; minimum 0; maximum 1022.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "branchingFactor": 2,
    "depth": 2,
    "minimumLeafValue": 0,
    "maximumLeafValue": 9,
    "maximizing": false,
    "seed": 42
  },
  "output": {
    "rootId": 0,
    "nodes": [
      {
        "id": 0,
        "depth": 0,
        "kind": "branch",
        "children": [
          1,
          4
        ]
      },
      {
        "id": 1,
        "depth": 1,
        "kind": "branch",
        "children": [
          2,
          3
        ]
      },
      {
        "id": 2,
        "depth": 2,
        "kind": "leaf",
        "value": 6
      },
      {
        "id": 3,
        "depth": 2,
        "kind": "leaf",
        "value": 4
      },
      {
        "id": 4,
        "depth": 1,
        "kind": "branch",
        "children": [
          5,
          6
        ]
      },
      {
        "id": 5,
        "depth": 2,
        "kind": "leaf",
        "value": 8
      },
      {
        "id": 6,
        "depth": 2,
        "kind": "leaf",
        "value": 6
      }
    ],
    "leafCount": 4,
    "treeJson": "{\"children\":[{\"children\":[{\"value\":6},{\"value\":4}]},{\"children\":[{\"value\":8},{\"value\":6}]}]}",
    "result": {
      "bestValue": 6,
      "evaluatedLeaves": 3,
      "prunedBranches": 1
    },
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Leaf count is limited to 512. The tree is an abstract deterministic perfect-information model; sampled payoffs do not represent a game engine, stochastic outcomes, or strategic learning.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Minimax Game Tree Test Case Generator — tool.io
Calculation or validation: game-search/alpha-beta — tool.io

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