How to represent a large decision tree? A system makes a decision basing on a large number of varied factors, following a "live" decision tree - one that is (independently, through other subsystem) updated with new decisions, new situations.

The individual decisions can be recorded as a kind of structure:


decision function
node to activate if decision is positive
node to activate if decision is negative


and a node can be another decision record, or a conclusion.

This isn't entirely a binary tree, as many decisions may lead to the same conclusion - each node has two children, but may have many parents.

There is absolutely no problem storing the tree in memory - it can be database records or entries of a map, or just a list. It's perfectly sufficient for the machine.

The problem here is building the subsystem that expands the decision tree - and in particular, having a human operator understand the structure being built, to be able to tune, guide, fix, adjust it: debugging the AI learning process.

The question is: how to represent that data in a human-readable way, that emphasizes the flow of the graph?

a non-working example of the answer is Concept map - in this case it only goes so far; with more than thirty or so nodes, it becomes a jumbled mess, especially if the number of cross-connections (multiple parents) becomes significant. Maybe there exists some way of laying it out or slicing it to make it clearer...?
The question is: how to represent that data in a human-readable way, that emphasizes the flow of the graph?


Train a reasoning engine to understand the decision tree for you.

Observe how IBM Watson/The Debater can 


Receive a particular question
Find and read Wikipedia articles related to the question
Understand parts of those articles and generate human-relevant arguments for you.


Follow these steps:


Develop your decision tree however you normally would.
Train a reasoning engine that can output natural language about concepts within decision trees.
Apply reason engine from step one to decision tree in step one; repeat.