====== Solving problems by search ======

Put your solution into a ''search_agents.py'' file and upload it. See deadlines in https://cw.felk.cvut.cz/brute

===== HW: 02_search1 =====
Implement a Breadth-First agent and Uniform-Cost agent

===== HW 03_search2 =====
Implement an Iterative-deepening agent and the A* algorithm with at least one heuristics. You can implement more than one heuristic.

===== grading =====
You algorithms will be tested on several maps checking whether they find a valid path and the optimal one. Manual evaluation will reflect readability and efficiency of your implementation.
===== support code =====

The code essentially defines the problem, provides a necessary interface and basic visualization for easier development. The basic usage is demonstrated in the ''sandbox.py''. You will probably need to define a Node class and some data structures for keeping/organizing the Nodes.

[[https://cw.felk.cvut.cz/courses/be5b33kui/kuimaze.zip|kuimaze package (zip-archive)]] contains also some maps and package documentation, which is also available [[https://cw.felk.cvut.cz/courses/be5b33kui/kuimaze_doc/|on-line]]. The package is a bit more general it contains more methods we need at the moment.

The basic interface/communication with the problem provide the following methods:
<code python>
problem = kuimaze.Maze('map.png') # create a problem (object)
state = problem.get_start_state() # get started
problem.is_goal_state(state)      # does the state belong to the goal set?
</code>
Loop over all actions that are possible for the given state.
<code python>
for action in problem.get_actions(state):
</code>
Get result of an ''action''
<code python>
child_state, transition_cost = problem.result(state, action)
</code>
The basic visualisation and how to construct the path is shown in ''sandbox.py''.


**Some screenshots:**

{{:courses:be5b33kui:labs:search:normal1-screenshot.png|}}

Probably a better solution:

{{:courses:be5b33kui:labs:search:normal1-bfs.png |}}