- updating Readme.txt to reflect release 0.95 notes. · aimacode/aima-java@a982000
1-AIMA JAVA Notes By Ravi(magesmail@yahoo.com)
1+AIMA JAVA Notes By Ravi(magesmail@yahoo.com) and Ciaran (ctjoreilly@gmail.com).
2233#summary some notes
4455= Introduction =
66The 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.
778-Current release is 0.94:<br>
8+Current release is 0.95:<br>
9+This is our first release containing GUIs (thanks to Ruediger Lunde):<br>
10+ - aima.gui.applications.VacuumAppDemo<br>
11+ Provides a demo of the different agents described in Chapter 2 and 3
12+ for tackling the Vacuum World.<br>
13+ - aima.gui.applications.search.map.RoutePlanningAgentAppDemo<br>
14+ Provides a demo of the different agents/search algorithms described
15+ in Chapters 3 and 4, for tackling route planning tasks within
16+ simplified Map environments.<br>
17+ - aima.gui.framework.SimpleAgentAppDemo<br>
18+ Provides a basic example of how to create your own Agent based
19+ demonstrations based on the provided framework.<br>
20+<br>
21+This will also be our last full release based on the 2nd edition of AIMA.
22+We are currently in the planning phases to re-organize this project based on the 3rd edition of AIMA, which should be available soon.
23+24+Previous release is 0.94:<br>
925This is a patch release for the FOL Logic and includes the following fixes:<br>
1026 - Fixed subtle defect in Model Elimination inference algorithm, which caused it to miss portions of the search space.<br>
1127 - 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>
@@ -16,7 +32,7 @@ It includes:<br>
1632 - a completion of the First Order Logic concepts from Chapter 9.<br>
1733 - the addition of the LRTA Agent from Chapter 4.<br>
183419- Note: If running the unite tests be sure to include the vm arguments:
35+ Note: If running the unit tests be sure to include the vm arguments:
2036 -Xms256m -Xmx1024m
2137 as some of the First Order Logic algorithms (i.e. FOLTFMResolution) are
2238 memory hungry.
@@ -29,38 +45,23 @@ It includes a rewrite of the neural network algorithms (in the earlier version t
2945Heuristics are now doubles (vs ints in the old version).
3046One minor change is that I've dropped the make file. Please use [http://ant.apache.org/ant ant]
314732-==bug reports - acknowledgment ==
33-34-The following people sent in excellent comments and bug reports. Thank you!!!!
35- * Ali Tozan
36-37- * Carl Anderson, Senior Scientist, ArchimedesModel.com
38-39- * Don Cochrane from (?) University
40-41- * Mike Angelotti from Miami University
42-43- * Chad Carff ,University of Western Florida . EXCELLENT test cases . thank you .
44-45- * Dr .Eman El-Sheikh, Ph.D.,University of Western Florida
46-47- * Ravindra Guravannavar, Aztec Software,Bangalore
48-49- * Cameron Jenkins,University Of New Orleans
50-51- * Nils Knoblauch (Project Manager, Camline) - winner of the No Prize for the best bug report ! Thanks!
52-53- * Phil Snowberger, Artificial Intelligence and Robotics Laboratory,University of Notre Dame
54-55-48+==Bug Reports - acknowledgment ==
564950+The following people sent in excellent comments and bug reports. Thank you!!!!<br>
51+ * Ali Tozan<br>
52+ * Carl Anderson, Senior Scientist, ArchimedesModel.com<br>
53+ * Don Cochrane from (?) University<br>
54+ * Mike Angelotti from Miami University<br>
55+ * Chad Carff ,University of Western Florida . EXCELLENT test cases . thank you.<br>
56+ * Dr .Eman El-Sheikh, Ph.D.,University of Western Florida<br>
57+ * Ravindra Guravannavar, Aztec Software,Bangalore<br>
58+ * Cameron Jenkins,University Of New Orleans<br>
59+ * Nils Knoblauch (Project Manager, Camline) - winner of the No Prize for the best bug report ! Thanks!<br>
60+ * Phil Snowberger, Artificial Intelligence and Robotics Laboratory,University of Notre Dame<br>
57615862= Details =
596360-61-6264==Build Instructions==
63-6465If you just want to use the classes, all you need to do is put the aima-java/build directory on your CLASSPATH.
65666667if you want to rebuild from source, run the unit tests etc follow the instructions below.
@@ -78,26 +79,22 @@ To build from the command line,
7879 # put [http://prdownloads.sourceforge.net/junit/junit3.8.1.zip?download junit 3.8.1 (note the version number)] on the classpath
7980 # type 'ant'
808181-8282I have included the eclipse.classpath and .projectfiles for those who use [http://www.eclipse.org eclipse] .
83838484==Code Navigation==
8585 # To understand how a particular feature works , FIRST look at the demo files.There are four main demo files SearchDemo , LogicDemo ,ProbabilityDemo and LearningDemo.
8686 # If the Demo Files don't exist yet , look at the unit tests . they often cover much of how a particular feature works .
8787 # 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 .
888889-9089==Notes on Search==
919092-9391To solve a problem with (non CSP )Search .
9492 # you need to write four classes .
9593 # 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 .
9694 # 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
9795 # 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.
9896 # 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.
9997100-10198that is all you need to do (unless you plan to write a different search than is available in the code base ).
10299103100To actually search you need to
@@ -126,6 +123,7 @@ A good example (from the NQueens Demo ) is
126123 }
127124 }
128125}}}
126+129127==Search Inheritance Trees ==
130128131129there are two inheritance trees in Search. one deals with "mechanism" of search.
@@ -148,8 +146,6 @@ The second tree deals with the search instances you can use to solve a problem.
148146149147etc
150148151-152-153149So if you see a declaration like
154150"SimulatedAnnealingSearch extends NodeExpander implements Search" , do not be confused.
155151@@ -162,8 +158,6 @@ Again, if you get confused, look at the demos.
162158163159164160==Logic Notes==
165-166-167161The 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.
168162169163To 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.
@@ -181,8 +175,6 @@ Except elimination-ask, the rest of the algorithms from chapter 13 and 14 have b
181175==LearningNotes==
182176183177===Main Classes and responsibilities===
184-185-186178A <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.
187179188180An Example is a collection of Attributes. Each example is a data point for Supervised Learning .