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AIMA-Java Core Algorithms from the book Artificial Intelligence a Modern Approach 3rd Ed.

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package aima.core.learning.knowledge;

import java.util.List;

import aima.core.logic.fol.kb.FOLKnowledgeBase;

/**
 * Artificial Intelligence A Modern Approach (3rd Edition): Figure 19.2, page
 * 771.

 * 

 * 
 *  * function CURRENT-BEST-LEARNING(examples, h) returns a hypothesis or fail
 * 
 *   if examples is empty then
 *      return h
 *   e <- FIRST(examples)
 *   if e is consistent with h then
 *      return CURRENT-BEST-LEARNING(REST(examples), h)
 *   else if e is a false positive for h then
 *     for each h' in specializations of h consistent with examples seen so far do
 *       h'' <- CURRENT-BEST-LEARNING(REST(examples), h')
 *       if h'' != fail then return h''
 *   else if e is a false negative for h then
 *     for each h' in generalization of h consistent with examples seen so far do
 *       h'' <- CURRENT-BEST-LEARNING(REST(examples), h')
 *       if h'' != fail then return h''
 *   return fail
 * 
 * 
 * Figure 19.2 The current-best-hypothesis learning algorithm. It searches for a
 * consistent hypothesis that fits all the examples and backtracks when no
 * consistent specialization/generalization can be found. To start the
 * algorithm, any hypothesis can be passed in; it will be specialized or
 * generalized as needed.
 * 
 * @author Ciaran O'Reilly
 * 
 */
public class CurrentBestLearning {
	@SuppressWarnings("unused")
	private FOLDataSetDomain folDSDomain = null;
	private FOLKnowledgeBase kbForLearning = null;

	//
	// PUBLIC METHODS
	//
	public CurrentBestLearning(FOLDataSetDomain folDSDomain,
			FOLKnowledgeBase kbForLearning) {
		this.folDSDomain = folDSDomain;
		this.kbForLearning = kbForLearning;
	}

	// TODO - Implement!!!
	public Hypothesis currentBestLearning(List examples) {

		// TODO-use the default from pg 769 for now.
		String c1 = "patrons(v,Some)";
		String c2 = "patrons(v,Full) AND (hungry(v) AND type(v,French))";
		String c3 = "patrons(v,Full) AND (hungry(v) AND (type(v,Thai) AND fri_sat(v)))";
		String c4 = "patrons(v,Full) AND (hungry(v) AND type(v,Burger))";
		String sh = "FORALL v (will_wait(v) <=> (" + c1 + " OR (" + c2
				+ " OR (" + c3 + " OR (" + c4 + ")))))";

		Hypothesis h = new Hypothesis(kbForLearning.tell(sh));

		return h;
	}

	//
	// PRIVATE METHODS
	//
}