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- updating Readme.txt to reflect release 0.95 notes.
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ctjoreilly committed Oct 4, 2009
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AIMA JAVA Notes By Ravi(magesmail@yahoo.com)
AIMA JAVA Notes By Ravi(magesmail@yahoo.com) and Ciaran (ctjoreilly@gmail.com).

#summary some notes

= Introduction =
The latest (and ever evolving) code can be found at http://code.google.com/p/aima-java/. if you notice a bug please try checking out the latest version from the svn repository to see if it persists.

Current release is 0.94:<br>
Current release is 0.95:<br>
This is our first release containing GUIs (thanks to Ruediger Lunde):<br>
- aima.gui.applications.VacuumAppDemo<br>
Provides a demo of the different agents described in Chapter 2 and 3
for tackling the Vacuum World.<br>
- aima.gui.applications.search.map.RoutePlanningAgentAppDemo<br>
Provides a demo of the different agents/search algorithms described
in Chapters 3 and 4, for tackling route planning tasks within
simplified Map environments.<br>
- aima.gui.framework.SimpleAgentAppDemo<br>
Provides a basic example of how to create your own Agent based
demonstrations based on the provided framework.<br>
<br>
This will also be our last full release based on the 2nd edition of AIMA.
We are currently in the planning phases to re-organize this project based on the 3rd edition of AIMA, which should be available soon.

Previous release is 0.94:<br>
This is a patch release for the FOL Logic and includes the following fixes:<br>
- Fixed subtle defect in Model Elimination inference algorithm, which caused it to miss portions of the search space.<br>
- Improved the performance of both theorem provers, in particular added support for forward and backward subsumption elimination, which improves significantly the performance and use of the OTTER Like theorem prover.<br>
Expand All @@ -16,7 +32,7 @@ It includes:<br>
- a completion of the First Order Logic concepts from Chapter 9.<br>
- the addition of the LRTA Agent from Chapter 4.<br>

Note: If running the unite tests be sure to include the vm arguments:
Note: If running the unit tests be sure to include the vm arguments:
-Xms256m -Xmx1024m
as some of the First Order Logic algorithms (i.e. FOLTFMResolution) are
memory hungry.
Expand All @@ -29,38 +45,23 @@ It includes a rewrite of the neural network algorithms (in the earlier version t
Heuristics are now doubles (vs ints in the old version).
One minor change is that I've dropped the make file. Please use [http://ant.apache.org/ant ant]

==bug reports - acknowledgment ==

The following people sent in excellent comments and bug reports. Thank you!!!!
* Ali Tozan

* Carl Anderson, Senior Scientist, ArchimedesModel.com

* Don Cochrane from (?) University

* Mike Angelotti from Miami University

* Chad Carff ,University of Western Florida . EXCELLENT test cases . thank you .

* Dr .Eman El-Sheikh, Ph.D.,University of Western Florida

* Ravindra Guravannavar, Aztec Software,Bangalore

* Cameron Jenkins,University Of New Orleans

* Nils Knoblauch (Project Manager, Camline) - winner of the No Prize for the best bug report ! Thanks!

* Phil Snowberger, Artificial Intelligence and Robotics Laboratory,University of Notre Dame


==Bug Reports - acknowledgment ==

The following people sent in excellent comments and bug reports. Thank you!!!!<br>
* Ali Tozan<br>
* Carl Anderson, Senior Scientist, ArchimedesModel.com<br>
* Don Cochrane from (?) University<br>
* Mike Angelotti from Miami University<br>
* Chad Carff ,University of Western Florida . EXCELLENT test cases . thank you.<br>
* Dr .Eman El-Sheikh, Ph.D.,University of Western Florida<br>
* Ravindra Guravannavar, Aztec Software,Bangalore<br>
* Cameron Jenkins,University Of New Orleans<br>
* Nils Knoblauch (Project Manager, Camline) - winner of the No Prize for the best bug report ! Thanks!<br>
* Phil Snowberger, Artificial Intelligence and Robotics Laboratory,University of Notre Dame<br>

= Details =



==Build Instructions==

If you just want to use the classes, all you need to do is put the aima-java/build directory on your CLASSPATH.

if you want to rebuild from source, run the unit tests etc follow the instructions below.
Expand All @@ -78,26 +79,22 @@ To build from the command line,
# put [http://prdownloads.sourceforge.net/junit/junit3.8.1.zip?download junit 3.8.1 (note the version number)] on the classpath
# type 'ant'


I have included the eclipse.classpath and .projectfiles for those who use [http://www.eclipse.org eclipse] .

==Code Navigation==
# To understand how a particular feature works , FIRST look at the demo files.There are four main demo files SearchDemo , LogicDemo ,ProbabilityDemo and LearningDemo.
# If the Demo Files don't exist yet , look at the unit tests . they often cover much of how a particular feature works .
# If all else fails , write to me . Comprehensive documentation, both java doc and otherwise are in the pipeline , but will probably have to wait till I finish the code .


==Notes on Search==


To solve a problem with (non CSP )Search .
# you need to write four classes .
# a class that represents the Problem state .This class is independent of the framework and does NOT need to subclass anything . Let us, for the rest of these instruction, assume you are going to solve the NQueens problem . So in this step you need to write something like aima.search.nqueens.NQueensBoard .
# a subclass of aima.search.framework.GoalTest.This implements only a single function ---boolean isGoalState(Object state); The parameter state is an instance of the class you created in step 1-a above. For the NQueensProblem you would need to write something like aima.search.nqueens.NqueensBoardTest
# a subclass of aima.search.framework.SuccessorFunction .This generates a stream of Successors where a Successor is an object that represents an (action, resultantState) pair. In this release of the code the action is a String (something like "placeQueenAt4,4" and the resultant State is an instance of the class you create in step 1.a . An example is aima.search.nqueens.NQueensSuccessorFunction.
# If you need to do an informed search, you should create a fourth class which subclasses aima.search.framework.HeuristicFunction. This implements a single function int getHeuristicValue(Object state); keep in mind that the heuristic should DECREASE as the goal state comes nearer . For the NQueens problem, you need to write something like aima.search.nqueens.QueensToBePlacedHeuristic.


that is all you need to do (unless you plan to write a different search than is available in the code base ).

To actually search you need to
Expand Down Expand Up @@ -126,6 +123,7 @@ A good example (from the NQueens Demo ) is
}
}
}}}

==Search Inheritance Trees ==

there are two inheritance trees in Search. one deals with "mechanism" of search.
Expand All @@ -148,8 +146,6 @@ The second tree deals with the search instances you can use to solve a problem.

etc



So if you see a declaration like
"SimulatedAnnealingSearch extends NodeExpander implements Search" , do not be confused.

Expand All @@ -162,8 +158,6 @@ Again, if you get confused, look at the demos.


==Logic Notes==


The ONE thing you need to watch out for is that the Parsers are VERY finicky . If you get a lexing or parsing error, there is a high probability there is an error in your logic string.

To use First Order Logic, first you need to create a subclass of aima.logic.fol.FOLDomain which collects the constants, predicates, functions etc that you use to solve a particular problem.
Expand All @@ -181,8 +175,6 @@ Except elimination-ask, the rest of the algorithms from chapter 13 and 14 have b
==LearningNotes==

===Main Classes and responsibilities===


A <DataSet> is a collection of <Example>s .Wherever you see "examples" in plural in the text , the code uses a DataSet . This makes it easy to aggregate operations that work on collections of examples in one place.

An Example is a collection of Attributes. Each example is a data point for Supervised Learning .
Expand Down

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