Prohledávání stavového prostoru I: BFS & DFS

Cybernetics and Artificial Intelligence 2012/SS
Assignment:
Searching a State Space
Petr Benda, translated by Radek Píbil
Part A
Implement BFS and DFS algorithms for state-space search. If you implement them
using the OPEN and CLOSED sets (lists), the difference between them is minimal.
The output of the algorithm is meant to be the first path between start and target
state found. Use the provided libraries for the GUI. In case of matlab or c/c++ either
use comprehensive text output, or create a simple GUI yourself. Matlab also has a
pretty good support of Java applications so there might be a way for you to use both
Matlab and the GUI. The state-space is defined by a planar graph.
Guidelines for implementation:
1. Download the “software tool for students” from the CourseWare website. Use
NetBeans to open the project.
2. Open the BFS.java source in NetBeans IDE. Your implementation goes in
there.
3. You can run your program using a premade run configuration “bfs” for NB.
“bfs directx” is provided in case you cannot use OpenGL.
4. In case you have troubles using the project on your home PC, follow
the instructions “solving settings problems” on the CW website,
which is going to be periodically updated. The system can be ran
with console output only using the “noVisio” argument (see
runBFS_novisio.bat), which may be useful for debugging.
Notes for implementation:
Do not create any other files, all the code is supposed to go into BFS.java.
See javadoc.
The node.isTarget() methods return true if a node is the target.
Newly expanded edges are drawn automatically after calling expand() method.
You should know that the nodes accessed through the kui.Node interface have
hashCode and equals correctly implemented. It is not possible to create a
node class not implementing kui.Node.
In order to draw the path (the ordered list of nodes), it is necessary to return
it from the findWay() method.

Cybernetics and Artificial Intelligence 2012/SS
Your algorithm can be run without NetBeans IDE using the Application class as
the main class and the name of your class (fully qualified) as a parameter.
This method is also being used by the provided batch files.
In order to slow down the execution in order to see the progress of execution you
can use the following code:
try {
Thread.sleep(100); // 100 ms
} catch (InterruptedException ex) {
}
Using the visualization tool:
ex.printStackTrace();
Setting the start and target node can be done using the left mouse button. Zooming
in and out can be done with the mouse wheel. A region can be enlarged using
ctrl+left mouse button and drag selecting the desired region. The right mouse button
can be used to scroll around the map. The color of an expanded edge corresponds
with the time at which the expansion took place. The oldest edges are green,
followed by blue, purple, red and yellow for the newest.
How to submit?
1. The deadline for all three tasks (BFS, DFS, A*) is the next seminar.
2. Only BFS and A* are submitted into CW. DFS is evaluated by your teacher, in
person.
3. Create a zip archive, which contains the BFS.java and AStar.java source files
in “kui” directory. One file is ok as well, for a partial automatic test, but it is
necessary for you to submit both files in the end. Other files are not
admissible.
4. Your classes must not contain any slowdown code.
5. Upload the zip archive into the upload system (https://cw.felk.cvut.cz/upload).
The name of the archive is irrelevant.
6. An automatic test is executed. It takes 30s at most, and then you can
examine the results.
7. You can upload the zip archive into the upload system as many times as you
may require.

Cybernetics and Artificial Intelligence 2012/SS
Assignment:
Part B
Implement the A* algorithm. The result is going to be the shortest path between the
starting a target node specified by the user. Design and use a heuristic function. Use
the provided GUI libraries (if applicable). The state-space is specified by a planar
graph.
Guidelines for implementation:
1. Open the AStar.java source file in the project. This class implements the
InformedSearchingAlgorithm interface. In contrast with the previous task,
findWay() receives both the starting and target node.
2. Change the run configuration to “astar” (hit the arrow next to the combo box
with “” and choose “astar”).
Notes for implementation:
Do not create any other files, put all the necessary code into the AStar.java
source file.
Questions:
Which of the algorithms usually (in our case) returns the shortest path?
How many states the BFS and DFS algorithms are going to expand in the
worst case?
In what sense is the path found by BFS the shortest one?
What conditions must the heuristic function satisfy in order to be sure that the
A* algorithm actually returns the shortest path with certainty?
What is a monotonic heuristic function?
What heuristic function have you chosen?
Is it necessary to implement the algorithm in its full generality, or is it possible
to perform some simplifications?
How to submit?
1. The deadline for all three tasks (BFS, DFS, A*) is the next seminar.
2. Only BFS and A* are submitted into CW. DFS is evaluated by your teacher, in
person.

Cybernetics and Artificial Intelligence 2012/SS
3. Create a zip archive, which contains the BFS.java and AStar.java source files
in “kui” directory. One file is ok as well, for a partial automatic test, but it is
necessary for you to submit both files in the end. Other files are not
admissible.
4. Your classes must not contain any slowdown code.
5. Upload the zip archive into the upload system (https://cw.felk.cvut.cz/upload).
The name of the archive is irrelevant.
6. An automatic test is executed. It takes 30s at most, and then you can
examine the results.
7. You can upload the zip archive into the upload system as many times as you
may require.