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...?

  • $\begingroup$ What sort of audience is this going to? General public? Computer scientists / programmers? Other AI people? $\endgroup$
    – Avik Mohan
    Aug 30 '16 at 12:53
  • $\begingroup$ @AvikMohan: The expert system: Professional; the expert system is used for diagnostics of a complex device; also the conclusions will be used by the device itself to undertake actions. The data visualisation (about which is the question): developers of the system. $\endgroup$
    – SF.
    Aug 30 '16 at 14:19

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:

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

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