From afb5618d5aa5f6e1145c67f88c634d4a7b47f555 Mon Sep 17 00:00:00 2001
From: lukas <lukas.molzberger@gmx.de>
Date: Wed, 31 Jul 2019 18:08:56 +0200
Subject: [PATCH] Release 1.3.0

---
 pom.xml                                       |   4 +-
 .../aika/neuron/activation/Activation.java    |   1 -
 .../aika/neuron/activation/search/Option.java |  12 +-
 src/site/resources.html                       |  16 ++-
 src/site/usage.html                           | 117 ++++++++----------
 5 files changed, 69 insertions(+), 81 deletions(-)

diff --git a/pom.xml b/pom.xml
index e5a49a8bc..6473da0e2 100644
--- a/pom.xml
+++ b/pom.xml
@@ -5,7 +5,7 @@
     <groupId>org.aika-software</groupId>
     <artifactId>aika</artifactId>
     <packaging>jar</packaging>
-    <version>1.2.2-SNAPSHOT</version>
+    <version>1.3.0</version>
     <name>aika</name>
     <url>http://aika.network</url>
     <description>An artificial intelligence for knowledge acquisition</description>
@@ -30,7 +30,7 @@
         <url>https://github.com/aika-algorithm/aika</url>
         <connection>scm:git:git://github.com/aika-algorithm/aika.git</connection>
         <developerConnection>scm:git:git@github.com:aika-algorithm/aika.git</developerConnection>
-        <tag>aika-1.1.0</tag>
+        <tag>aika-1.3.0</tag>
     </scm>
 
 
diff --git a/src/main/java/network/aika/neuron/activation/Activation.java b/src/main/java/network/aika/neuron/activation/Activation.java
index d24363cee..78d0f23b6 100644
--- a/src/main/java/network/aika/neuron/activation/Activation.java
+++ b/src/main/java/network/aika/neuron/activation/Activation.java
@@ -955,7 +955,6 @@ public enum Mode {OLD, NEW}
 
 
     public void saveState(SearchNode sn) {
-        currentOption.fixed = true;
         currentOption = new Option(currentOption, this, sn);
 
         if (sn.getModifiedActivations() != null) {
diff --git a/src/main/java/network/aika/neuron/activation/search/Option.java b/src/main/java/network/aika/neuron/activation/search/Option.java
index 6e72feb31..3ec6444f4 100644
--- a/src/main/java/network/aika/neuron/activation/search/Option.java
+++ b/src/main/java/network/aika/neuron/activation/search/Option.java
@@ -41,19 +41,17 @@ public class Option implements Comparable<Option> {
 
     public static final State INITIAL_STATE = new State(0.0, Double.MAX_VALUE, 0.0, null, 0.0);
 
-    public State state = INITIAL_STATE;
+    private State state = INITIAL_STATE;
 
     public Activation act;
     public SearchNode searchNode;
 
-    public Option parent;
-    public List<Option> children = new ArrayList<>();
-
-    public boolean fixed = false;
+    private Option parent;
+    private List<Option> children = new ArrayList<>();
 
     public Decision decision;
 
-    public double weight;
+    private double weight;
     public double remainingWeight;
     public int cacheFactor = 1;
     public double p;
@@ -81,7 +79,6 @@ public Option(Option parent, Activation act, SearchNode sn) {
 
 
     public boolean setState(State s) {
-        assert !fixed;
         if(state.equalsWithWeights(s)) {
             return false;
         }
@@ -114,7 +111,6 @@ public boolean isQueued() {
 
     public void restoreState(Activation.Mode m) {
         act.currentOption = (m == Activation.Mode.OLD ? parent : this);
-        assert act.currentOption.fixed;
     }
 
 
diff --git a/src/site/resources.html b/src/site/resources.html
index 6ec7d2065..2016b0859 100644
--- a/src/site/resources.html
+++ b/src/site/resources.html
@@ -56,8 +56,6 @@ <h2>Related Resources</h2>
 			<p>
 				<a href="https://medium.com/p/on-integrating-symbolic-inference-into-deep-neural-networks-22ed13ebbba9?fbclid=IwAR0Akwh1d8LCnlIQjvalGrmhcsSjzGl825dm3GhGPJ7PTY3cM9rHDkVtla0">On integrating symbolic inference into deep neural networks</a><br/>
 
-				Workshop: <a href="http://aika.network/Aika-Workshop.pdf">Einführung in den Aika-Algorithmus</a><br/>
-
 				<a href="https://data-science-blog.com/blog/2017/10/25/aika-ein-semantisches-neuronales-netzwerk/">A data science blog article about the Aika algorithm written in German.</a><br/>
 
 				<a href="https://jaxenter.de/texte-verstehen-mit-machine-learning-58487">A JAXenter article about the Aika algorithm.</a><br/>
@@ -69,16 +67,26 @@ <h2>Related Resources</h2>
 
 			<h2>Download</h2>
 			<p>
-				<a href="http://aika.network/download/aika-1.2.0.jar">Version 1.2.0</a> <br/>
+				<a href="http://aika.network/download/aika-1.3.0.jar">Version 1.3.0</a> <br/>
 			</p>
 
 			<h2>API Documentation</h2>
 
 			<p>
-				<a href="http://aika.network/apidocs/index.html">JavaDoc v1.2.0</a>
+				<a href="http://aika.network/apidocs/index.html">JavaDoc v1.3.0</a>
 			</p>
 
 			<h2>Release Notes</h2>
+			<h3>Aika Version 1.3.0 (2019-07-31)</h3>
+			<ul>
+				<li>
+					Major simplification of the interpretation search.
+				</li>
+				<li>
+					Code reorganization.
+				</li>
+			</ul>
+
 			<h3>Aika Version 1.2.0 (2019-06-07)</h3>
 			<ul>
 				<li>
diff --git a/src/site/usage.html b/src/site/usage.html
index 066806696..473aedf4e 100644
--- a/src/site/usage.html
+++ b/src/site/usage.html
@@ -58,7 +58,7 @@ <h3>Maven POM</h3>
                 &lt;dependency&gt;<br/>
                 &nbsp;&nbsp;&nbsp;&nbsp;&lt;groupId&gt;org.aika-software&lt;&#47;groupId&gt;<br/>
                 &nbsp;&nbsp;&nbsp;&nbsp;&lt;artifactId&gt;aika&lt;&#47;artifactId&gt;<br/>
-                &nbsp;&nbsp;&nbsp;&nbsp;&lt;version&gt;1.1.0&lt;&#47;version&gt;<br/>
+                &nbsp;&nbsp;&nbsp;&nbsp;&lt;version&gt;1.3.0&lt;&#47;version&gt;<br/>
                 &lt;&#47;dependency&gt;<br/>
                 </b>
             </p>
@@ -80,7 +80,7 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
     HashMap&lt;String, Neuron&gt; inputNeurons = new HashMap<>();
 
     for(String word: new String[] {"jackson", "cook"}) {
-        Neuron in = m.createNeuron("W-" + word);
+        Neuron in = m.createNeuron("W-" + word, INPUT);
 
         inputNeurons.put(word, in);
     }
@@ -93,9 +93,9 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
             <div class="prettyprint-code">
             <pre class="prettyprint">
 				<code class="language-java">
-    Neuron forenameCategory = m.createNeuron("C-forename");
-    Neuron surnameCategory = m.createNeuron("C-surname");
-    Neuron inhibitingN = m.createNeuron("INHIB");
+    Neuron forenameCategory = m.createNeuron("C-forename", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT);
+    Neuron surnameCategory = m.createNeuron("C-surname", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT);
+    Neuron inhibitingN = m.createNeuron("INHIB", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT);
                 </code>
 			</pre>
             </div>
@@ -114,10 +114,8 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
             <pre class="prettyprint">
 				<code class="language-java">
     Neuron cookSurnameEntity = Neuron.init(
-            m.createNeuron("E-cook (surname)"),
+            m.createNeuron("E-cook (surname)", EXCITATORY, RECTIFIED_HYPERBOLIC_TANGENT),
             6.0, // adjusts the bias
-            RECTIFIED_HYPERBOLIC_TANGENT,
-            EXCITATORY,
             new Synapse.Builder() // Requires the word to be recognized
                     .setSynapseId(0)
                     .setNeuron(inputNeurons.get("cook"))
@@ -165,8 +163,6 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
     Neuron.init(
             forenameCategory,
             0.0,
-            RECTIFIED_LINEAR_UNIT,
-            EXCITATORY,
             new Synapse.Builder() // In this example there is only one forename considered.
                     .setSynapseId(0)
                     .setNeuron(jacksonForenameEntity)
@@ -180,8 +176,6 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
     Neuron.init(
             surnameCategory,
             0.0,
-            RECTIFIED_LINEAR_UNIT,
-            EXCITATORY,
             new Synapse.Builder()
                     .setSynapseId(0)
                     .setNeuron(cookSurnameEntity)
@@ -204,8 +198,6 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
     Neuron.init(
             inhibitingN,
             0.0,
-            RECTIFIED_LINEAR_UNIT,
-            INHIBITORY,
             new Synapse.Builder().setNeuron(cookProfessionEntity)
                     .setSynapseId(0)
                     .setWeight(1.0)
@@ -279,23 +271,27 @@ <h3>Named Entity Recognition / Entity Resolution example</h3>
             <pre class="prettyprint">
 				<code class="language-java">
 
-Activation ID - Final Decision - Slots | Identity - Neuron Label - Logic Layer - Upper Bound -
+Activation ID - Neuron Type - Final Decision - Slots (Ranges) | Identity - Neuron Label - Logic Layer - Upper Bound -
 Value | Sum | Weight - Input Value | Target Value
 
-    ...
-3 - SELECTED - (0:4, 1:12) () - C-forename - OR[] - V:1.0 Net:1.0 W:0.0
-4 - SELECTED - (0:4, 1:12) () - INHIB - OR[] - V:1.0 Net:1.0 W:0.0
-1 - SELECTED - (0:4, 1:12) () - W-jackson - OR[] - V:1.0 Net:0.0 W:0.0 - IV:1.0
-2 - SELECTED - (0:4, 1:12) () - E-jackson (forename) - V:1.0 Net:6.0 W:6.0
-5 - EXCLUDED - (0:4, 1:12) () - E-jackson (city) -
-8 - SELECTED - (0:12, 1:17) () - C-surname - OR[] - V:1.0 Net:1.0 W:0.0
-9 - SELECTED - (0:12, 1:17) () - INHIB - OR[] - V:1.0 Net:1.0 W:0.0
-6 - SELECTED - (0:12, 1:17) () - W-cook - OR[] - V:1.0 Net:0.0 W:0.0 - IV:1.0
-7 - SELECTED - (0:12, 1:17) () - E-cook (surname) - V:1.0 Net:8.0 W:8.0
-10 - EXCLUDED - (0:12, 1:17) () - E-cook (profession) -
-    ...
-
- Final SearchNode:8  WeightSum:13.999
+0   INPUT      -          - (0:0, 1:4) "mr. " () - W-mr. - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+4   INHIBITORY -          - (0:4, 1:12) "jackson " () - C-forename - V:1.0 UB:1.0 Net:1.0 W:0.0
+5   INHIBITORY -          - (0:4, 1:12) "jackson " () - INHIB - V:1.0 UB:1.0 Net:1.0 W:0.0
+1   INPUT      -          - (0:4, 1:12) "jackson " () - W-jackson - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+2   EXCITATORY - SELECTED - (0:4, 1:12) "jackson " () - E-jackson (forename) - V:1.0 UB:1.0 Net:6.0 W:6.0
+3   EXCITATORY - EXCLUDED - (0:4, 1:12) "jackson " () - E-jackson (city) - V:0.0 UB:0.0 Net:-95.0 W:0.0
+9   INHIBITORY -          - (0:12, 1:17) "cook " () - C-surname - V:1.0 UB:1.0 Net:1.0 W:0.0
+10  INHIBITORY -          - (0:12, 1:17) "cook " () - INHIB - V:1.0 UB:1.0 Net:1.0 W:0.0
+6   INPUT      -          - (0:12, 1:17) "cook " () - W-cook - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+7   EXCITATORY - SELECTED - (0:12, 1:17) "cook " () - E-cook (surname) - V:1.0 UB:1.0 Net:6.0 W:6.0
+8   EXCITATORY - EXCLUDED - (0:12, 1:17) "cook " () - E-cook (profession) - V:0.0 UB:0.0 Net:-95.0 W:0.0
+11  INPUT      -          - (0:17, 1:21) "was " () - W-was - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+12  INPUT      -          - (0:21, 1:26) "born " () - W-born - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+13  INPUT      -          - (0:26, 1:29) "in " () - W-in - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+14  INPUT      -          - (0:29, 1:33) "new " () - W-new - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+15  INPUT      -          - (0:33, 1:38) "york " () - W-york - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+
+ Final SearchNode:6  WeightSum:12.0
                 </code>
 			</pre>
             </div>
@@ -316,19 +312,17 @@ <h3>Mutual exclusion example</h3>
         Model m = new Model();
 
         // Create the input neurons for the network.
-        Neuron inA = m.createNeuron("IN-A");
-        Neuron inB = m.createNeuron("IN-B");
-        Neuron inC = m.createNeuron("IN-C");
+        Neuron inA = m.createNeuron("IN-A", INPUT);
+        Neuron inB = m.createNeuron("IN-B", INPUT);
+        Neuron inC = m.createNeuron("IN-C", INPUT);
 
         // Instantiate the inhibitory neuron. Its inputs will be added later on.
         Neuron inhibN = m.createNeuron("INHIB");
 
         // Create three neurons that might be suppressed by the inhibitory neuron.
         Neuron pA = Neuron.init(
-                m.createNeuron("A"),
+                m.createNeuron("A", EXCITATORY),
                 3.0,
-                RECTIFIED_HYPERBOLIC_TANGENT,
-                EXCITATORY,
                 new Synapse.Builder()
                         .setSynapseId(0)
                         .setNeuron(inA)
@@ -352,10 +346,8 @@ <h3>Mutual exclusion example</h3>
         );
 
         Neuron pB = Neuron.init(
-                m.createNeuron("B"),
+                m.createNeuron("B", EXCITATORY),
                 5.0,
-                RECTIFIED_HYPERBOLIC_TANGENT,
-                EXCITATORY,
                 new Synapse.Builder()
                         .setSynapseId(0)
                         .setNeuron(inB)
@@ -379,10 +371,8 @@ <h3>Mutual exclusion example</h3>
         );
 
         Neuron pC = Neuron.init(
-                m.createNeuron("C"),
+                m.createNeuron("C", EXCITATORY),
                 2.0,
-                RECTIFIED_HYPERBOLIC_TANGENT,
-                EXCITATORY,
                 new Synapse.Builder()
                         .setSynapseId(0)
                         .setNeuron(inC)
@@ -409,8 +399,6 @@ <h3>Mutual exclusion example</h3>
         Neuron.init(
                 inhibN,
                 0.0,
-                RECTIFIED_LINEAR_UNIT,
-                INHIBITORY,
                 new Synapse.Builder()
                         .setSynapseId(0)
                         .setNeuron(pA)
@@ -443,10 +431,9 @@ <h3>Mutual exclusion example</h3>
                         .setRelation(EQUALS)
         );
 
-        Neuron outN = Neuron.init(m.createNeuron("OUT"),
+        Neuron outN = Neuron.init(
+                m.createNeuron("OUT", EXCITATORY),
                 0.0,
-                RECTIFIED_HYPERBOLIC_TANGENT,
-                EXCITATORY,
                 new Synapse.Builder()
                          .setSynapseId(0)
                         .setNeuron(pB)
@@ -486,17 +473,17 @@ <h3>Mutual exclusion example</h3>
 
 			<pre class="prettyprint">
 				<code class="language-java">
- Activation ID - Final Decision - Slots | Identity - Neuron Label - Upper Bound -
+ Activation ID - Neuron Type - Final Decision - Slots | Identity - Neuron Label - Upper Bound -
  Value | Net | Weight - Input Value | Target Value
 
-0 - SELECTED - (0:0, 1:1) () - IN-A - V:1.0 Net:0.0 W:0.0 - IV:1.0
-3 - SELECTED - (0:0, 1:1) () - IN-B - V:1.0 Net:0.0 W:0.0 - IV:1.0
-6 - SELECTED - (0:0, 1:1) () - IN-C - V:1.0 Net:0.0 W:0.0 - IV:1.0
-2 - SELECTED - (0:0, 1:1) () - INHIB - V:1.0 Net:1.0 W:0.0
-1 - EXCLUDED - (0:0, 1:1) () - A -
-4 - SELECTED - (0:0, 1:1) () - B - V:1.0 Net:5.0 W:5.0
-7 - EXCLUDED - (0:0, 1:1) () - C -
-5 - SELECTED - (0:0, 1:1) () - OUT - V:0.762 Net:1.0 W:0.0
+0   INPUT      -          - (0:0, 1:1) "f" () - IN-A - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+3   INPUT      -          - (0:0, 1:1) "f" () - IN-B - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+6   INPUT      -          - (0:0, 1:1) "f" () - IN-C - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+2   INHIBITORY -          - (0:0, 1:1) "f" () - INHIB - V:1.0 UB:1.0 Net:1.0 W:0.0
+1   EXCITATORY - EXCLUDED - (0:0, 1:1) "f" () - A - V:0.0 UB:0.0 Net:-97.0 W:0.0
+4   EXCITATORY - SELECTED - (0:0, 1:1) "f" () - B - V:1.0 UB:1.0 Net:5.0 W:5.0
+7   EXCITATORY - EXCLUDED - (0:0, 1:1) "f" () - C - V:0.0 UB:0.0 Net:-98.0 W:0.0
+5   INHIBITORY -          - (0:0, 1:1) "f" () - OUT - V:0.762 UB:0.762 Net:1.0 W:0.0
 
 Final SearchNode:10  WeightSum:5.0
 				</code>
@@ -518,16 +505,14 @@ <h3>Pattern matching example</h3>
 
         // Create an input neuron for every letter in this example.
         for(char c: new char[] {'a', 'b', 'c', 'd', 'e', 'f'}) {
-            Neuron in = m.createNeuron(c + "");
+            Neuron in = m.createNeuron(c + "", INPUT);
 
             inputNeurons.put(c, in);
         }
 
         Neuron pattern = Neuron.init(
-                m.createNeuron("BCDE"),
+                m.createNeuron("BCDE", EXCITATORY),
                 5.0,
-                RECTIFIED_HYPERBOLIC_TANGENT,
-                EXCITATORY,
                 new Synapse.Builder()
                         .setSynapseId(0)
                         .setNeuron(inputNeurons.get('b'))
@@ -605,15 +590,15 @@ <h3>Pattern matching example</h3>
             <div class="prettyprint-code">
             <pre class="prettyprint">
 				<code class="language-java">
-Activation ID - Final Decision - Slots | Identity - Neuron Label - Upper Bound -
+Activation ID - Neuron Type - Final Decision - Slots | Identity - Neuron Label - Upper Bound -
 Value | Net | Weight - Input Value | Target Value
 
-0 - SELECTED - (0:0, 1:2) () - a - V:1.0 Net:0.0 W:0.0 - IV:1.0
-1 - SELECTED - (0:2, 1:4) () - b - V:1.0 Net:0.0 W:0.0 - IV:1.0
-5 - SELECTED - (0:2, 1:10) () - BCDE - V:1.0 Net:5.0 W:0.0
-2 - SELECTED - (0:4, 1:6) () - c - V:1.0 Net:0.0 W:0.0 - IV:1.0
-3 - SELECTED - (0:6, 1:8) () - d - V:1.0 Net:0.0 W:0.0 - IV:1.0
-4 - SELECTED - (0:8, 1:10) () - e - V:1.0 Net:0.0 W:0.0 - IV:1.0
+0   INPUT      -          - (0:0, 1:2) "a " () - a - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+1   INPUT      -          - (0:2, 1:4) "b " () - b - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+5   EXCITATORY - SELECTED - (0:2, 1:10) "b c d e " () - BCDE - V:1.0 UB:1.0 Net:5.0 W:0.0
+2   INPUT      -          - (0:4, 1:6) "c " () - c - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+3   INPUT      -          - (0:6, 1:8) "d " () - d - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
+4   INPUT      -          - (0:8, 1:10) "e " () - e - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
 
  Final SearchNode:1  WeightSum:0.0
 				</code>