- merge -r134:192 https://aima-java.googlecode.com/svn/branches/20080811_Chp19_FOL_Sandbox C:/Active/eclipse3_4_ws/aima-java

- This is a complete implementation of Chapter 9. A Model Elimination inference algorithm has also been included.

Author: ctjoreilly

.classpath

@@ -1,7 +1,7 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<classpath>
-	<classpathentry kind="src" path="src"/>
-	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
-	<classpathentry kind="lib" path="/home/ravi/projects/java/javalibs/junit3.8.2/junit.jar"/>
-	<classpathentry kind="output" path="bin"/>
-</classpath>
+<?xml version="1.0" encoding="UTF-8"?>
+<classpath>
+	<classpathentry kind="src" path="src"/>
+	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
+	<classpathentry kind="con" path="org.eclipse.jdt.junit.JUNIT_CONTAINER/3"/>
+	<classpathentry kind="output" path="bin"/>
+</classpath>

License.txt

@@ -1,4 +1,4 @@
-Copyright (c) <2003-2008> <Ravi Mohan, Peter Norvig, Stuart Russell>
+Copyright (c) <2003-2008> <Ravi Mohan, Peter Norvig, Stuart Russell, Ciaran O'Reilly>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

ReadMe.txt

@@ -1,5 +1,9 @@
 AIMA JAVA Notes By Ravi([email protected])
 
+Note: If running the unite tests be sure to include the vm arguments:
+-Xms256m -Xmx1024m
+as some of the First Order Logic algorithms (i.e. FOLTFMResolution) are
+memory hungry. 
 
 version 0.92
 ***********

src/aima/learning/knowledge/CurrentBestLearning.java

@@ -0,0 +1,29 @@
+package aima.learning.knowledge;
+
+/**
+ * Artificial Intelligence A Modern Approach (2nd Edition): Figure 19.2, page 681.
+ * 
+ * <code>
+ * function CURRENT-BEST-LEARNING(examples) returns a hypothesis
+ * 
+ *   H <- any hypothesis consistent with the first example in examples.
+ *   for each remaining example in examples do
+ *     if e is false positive for H then
+ *       H <- choose a specialization of H consistent with examples
+ *     else if e is false negative for H then
+ *       H <- choose a generalization of H consistent with examples
+ *     if no consistent specialization/generalization can be found then fail
+ *   return H
+ * </code>
+ * 
+ * Figure 19.2 The current-best-hypothesis learning algorithm. It searches for a consistent
+ * hypothesis and backtracks when no consistent specialization/generalization can be found.
+ */
+
+/**
+ * @author Ciaran O'Reilly
+ * 
+ */
+public class CurrentBestLearning {
+
+}