Answers.Body: <blockquote>
    <p>The question is: how to represent that data in a human-readable way, that emphasizes the flow of the graph?</p>
  </blockquote>
  
  <p>Train a reasoning engine to understand the decision tree <strong>for</strong> you.</p>
  
  <p>Observe how <a href="http://researcher.ibm.com/researcher/view_group_pubs.php?grp=5443" rel="nofollow">IBM Watson/The Debater</a> can </p>
  
  <ul>
  <li>Receive a particular question</li>
  <li>Find and read Wikipedia articles related to the question</li>
  <li>Understand parts of those articles and generate human-relevant arguments <strong>for</strong> you.</li>
  </ul>
  
  <p>Follow these steps:</p>
  
  <ol>
  <li>Develop your decision tree however you normally would.</li>
  <li>Train a reasoning engine that can output natural language about concepts within decision trees.</li>
  <li>Apply reason engine from step one to decision tree in step one; repeat.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-30T18:28:10.103Z
Answers.LastActivityDate: 2016-08-30T18:28:10.103Z
Answers.UserDisplayName: Doxosophoi
Body: <p>A system makes a decision basing on a large number of <em>varied</em> factors, following a "live" decision tree - one that is (independently, through other subsystem) updated with new decisions, new situations.</p>
  
  <p>The individual decisions can be recorded as a kind of structure:</p>
  
  <ul>
  <li>decision function</li>
  <li>node to activate if decision is positive</li>
  <li>node to activate if decision is negative</li>
  </ul>
  
  <p>and a node can be another decision record, or a conclusion.</p>
  
  <p>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.</p>
  
  <p>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.</p>
  
  <p>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: <strong>debugging the AI learning process.</strong></p>
  
  <p>The question is: how to represent that data in a human-readable way, that emphasizes the flow of the graph?</p>
  
  <p>a non-working example of the answer is <a href="https://en.wikipedia.org/wiki/Concept_map" rel="nofollow">Concept map</a> - 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...?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-30T12:53:30.747Z
Comments.Text: What sort of audience is this going to? General public? Computer scientists / programmers? Other AI people?
Comments.UserDisplayName: Avik Mohan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-30T14:19:48.257Z
Comments.Text: @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.
Comments.UserDisplayName: SF.
Tags: <machine-learning><decision-theory><implementation>
Title: How to represent a large decision tree?
UserDisplayName: SF.
ThreadText: # How to represent a large decision tree?
  
  (Asked by: username_0 on 2016-08-30)
  
  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](https://en.wikipedia.org/wiki/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...?
  
  **Tags:** machine-learning, decision-theory, implementation
  > **Comments:**
  > **username_1**: What sort of audience is this going to? General public? Computer scientists / programmers? Other AI people?  
  > **username_0**: @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.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2016-08-30. Score: 1
  
  > 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](http://researcher.ibm.com/researcher/view_group_pubs.php?grp=5443) 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.
  
  
  ---
  
  