[jboss-svn-commits] JBL Code SVN: r14973 - labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver.
jboss-svn-commits at lists.jboss.org
jboss-svn-commits at lists.jboss.org
Sun Sep 9 12:06:09 EDT 2007
Author: ge0ffrey
Date: 2007-09-09 12:06:09 -0400 (Sun, 09 Sep 2007)
New Revision: 14973
Added:
labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Local_Search_Solver.xml
Modified:
labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Solver_introduction.xml
Log:
spelling corrector
Added: labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Local_Search_Solver.xml
===================================================================
--- labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Local_Search_Solver.xml (rev 0)
+++ labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Local_Search_Solver.xml 2007-09-09 16:06:09 UTC (rev 14973)
@@ -0,0 +1,33 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<section>
+ <title>Local search solver</title>
+
+ <section>
+ <title>Overview</title>
+
+ <para>A local search algoritm and the drools rule engine turn out to be a
+ really nice combination, because:</para>
+
+ <itemizedlist>
+ <listitem>
+ <para>A rule engine such as Drools is great for <emphasis
+ role="bold">calculating the score</emphasis> of a solution of a
+ planning problem. It make it easy to add additional soft or hard
+ constraints such as "a teacher shouldn't teach more then 7 hours a
+ day". However it tends to be too complex to use to actually find new
+ solutions.</para>
+ </listitem>
+
+ <listitem>
+ <para>A local search algoritm is great at <emphasis
+ role="bold">finding new improving solutions</emphasis> for a planning
+ problem, without brute-forcing every possibility. However it needs to
+ know the score of a solution and normally offers no support in
+ calculating that score.</para>
+ </listitem>
+ </itemizedlist>
+
+ <para>Drools-solver's local search implementation combines both and offers
+ additional support for benchmarking etc.</para>
+ </section>
+</section>
\ No newline at end of file
Modified: labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Solver_introduction.xml
===================================================================
--- labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Solver_introduction.xml 2007-09-09 15:48:32 UTC (rev 14972)
+++ labs/jbossrules/trunk/documentation/manual/en/Chapter-Solver/Section-Solver_introduction.xml 2007-09-09 16:06:09 UTC (rev 14973)
@@ -5,10 +5,10 @@
<section>
<title>Status of drools-solver</title>
- <para>Drools-solver is an experimental module of drools-solver. The API is
- far from stable and backward incompatible changes occur now and then. A
- recipe to upgrade and apply those API changes between versions will be
- maintained soon.</para>
+ <para>Drools-solver is an <emphasis role="bold">experimental</emphasis>
+ module of drools-solver. The API is far from stable and backward
+ incompatible changes occur now and then. A recipe to upgrade and apply
+ those API changes between versions will be maintained soon.</para>
</section>
<section>
@@ -55,7 +55,7 @@
</listitem>
</itemizedlist>
- <para>Ussually a planning problem consists out of a number of constraints.
+ <para>Usually a planning problem consists out of a number of constraints.
Generally, there are 3 types of constraints:</para>
<itemizedlist>
@@ -74,21 +74,21 @@
<listitem>
<para>A <emphasis role="bold">positive constraint (or
- reward)</emphasis> should be fullfilled if possible. For example:
+ reward)</emphasis> should be fulfilled if possible. For example:
<emphasis>Teacher B likes to teach on Monday
morning</emphasis>.</para>
</listitem>
</itemizedlist>
<para>The constraints define the score function of a planning problem.
- Ussually a planning problems has a very large number of possible
- solutions. Each solution has a score. Ussually, most solutions are not
- feasible, because they break a negative hard constraint. Ussually, of all
- the feasible solutions (if any), there is only 1 or very few optimal
+ Usually a planning problems has a very large number of possible solutions.
+ Each solution has a score. Usually, most solutions are not feasible,
+ because they break a negative hard constraint. Usually, of all the
+ feasible solutions (if any), there is only 1 or very few optimal
solutions.</para>
<para>The drools rule engine turns out to be a very good way to implement
- a score function a number of rule constaints.</para>
+ a score function a number of rule constraints.</para>
</section>
<section>
@@ -100,8 +100,8 @@
<section>
<title>Brute force</title>
- <para>Brute force creates and evaluates every possible solution,
- ussually by create a search tree.</para>
+ <para>Brute force creates and evaluates every possible solution, usually
+ by create a search tree.</para>
<para>Advantages:</para>
@@ -161,7 +161,10 @@
<section>
<title>Simplex</title>
- <para>Brute force is</para>
+ <para>Simplex turns all constraints into a big equation, which it than
+ transmutes into a mathematical function without local optima. It then
+ finds an optimal solution by finding an optima of that mathematical
+ function.</para>
<para>Advantages:</para>
@@ -175,7 +178,7 @@
<itemizedlist>
<listitem>
- <para>It's ussually rather complex and mathematical to implement
+ <para>It's usually rather complex and mathematical to implement
constraints.</para>
</listitem>
</itemizedlist>
@@ -196,7 +199,7 @@
search path and moves facts around to find a very good solution.</para>
<para>A vanilla local search can easily get stuck in a local optima, but
- improvements (such as tabu search and simulated annealing) adress this
+ improvements (such as tabu search and simulated annealing) address this
problem.</para>
<para>Advantages:</para>
@@ -226,9 +229,9 @@
</listitem>
<listitem>
- <para>If the perfect score is unknown (which is ussually the case),
+ <para>If the perfect score is unknown (which is usually the case),
it must be told when to stop looking (for example based on time
- spend, user imput, ...).</para>
+ spend, user input, ...).</para>
</listitem>
</itemizedlist>
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