ó s­gZc@s}dZddlZdd d„ƒYZd„Zd„Zd„Zd„Zdd „Zed „Z eZ eZ e Z eZ dS( st In search.py, you will implement generic search algorithms which are called by Pacman agents (in searchAgents.py). i˙˙˙˙Nt SearchProblemcBs2eZdZd„Zd„Zd„Zd„ZRS(sß This class outlines the structure of a search problem, but doesn't implement any of the methods (in object-oriented terminology: an abstract class). You do not need to change anything in this class, ever. cCstjƒdS(sA Returns the start state for the search problem. N(tutiltraiseNotDefined(tself((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyt getStartStatescCstjƒdS(sm state: Search state Returns True if and only if the state is a valid goal state. N(RR(Rtstate((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyt isGoalState$scCstjƒdS(sL state: Search state For a given state, this should return a list of triples, (successor, action, stepCost), where 'successor' is a successor to the current state, 'action' is the action required to get there, and 'stepCost' is the incremental cost of expanding to that successor. N(RR(RR((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyt getSuccessors,s cCstjƒdS(sź actions: A list of actions to take This method returns the total cost of a particular sequence of actions. The sequence must be composed of legal moves. N(RR(Rtactions((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pytgetCostOfActions7s(t__name__t __module__t__doc__RRRR (((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyRs    cCs>ddlm}|j}|j}||||||||gS(sœ Returns a sequence of moves that solves tinyMaze. For any other maze, the sequence of moves will be incorrect, so only use this for tinyMaze. i˙˙˙˙(t Directions(tgameR tSOUTHtWEST(tproblemR tstw((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyttinyMazeSearchAs  cCs‡ddlm}|j}|j}dG|jƒGHdG|j|jƒƒGHdG|j|jƒƒGH||||||||gStjƒ(sM Search the deepest nodes in the search tree first. Your search algorithm needs to return a list of actions that reaches the goal. Make sure to implement a graph search algorithm. To get started, you might want to try some of these simple commands to understand the search problem that is being passed in: i˙˙˙˙(R sStart:sIs the start a goal?sStart's successors:( RR RRRRRRR(RR RR((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pytdepthFirstSearchKs   cCstjƒdS(s5Search the shallowest nodes in the search tree first.N(RR(R((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pytbreadthFirstSearchdscCstjƒdS(s*Search the node of least total cost first.N(RR(R((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pytuniformCostSearchiscCsdS(s› A heuristic function estimates the cost from the current state to the nearest goal in the provided SearchProblem. This heuristic is trivial. i((RR((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyt nullHeuristicnscCstjƒdS(sFSearch the node that has the lowest combined cost and heuristic first.N(RR(Rt heuristic((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyt aStarSearchus((R RRRRRRtNoneRRtbfstdfstastartucs(((sG/Users/saia/public_html/classes/527-s18/proj/proj1/old/search/search.pyts +