Get the actions available for a given state. This is what you should use to obtain legal actions for a state

Returns a list of booleans indicating which ghosts are not yet captured. The first entry (a placeholder for Pacman's index) is alway

get the lower- and upper- bound for the arm angles returns (min,max) pair

get the lower- and upper- bound for the hand angles returns (min,max) pair

Returns an AgentState object for pacman (in game.py) state.pos gives the current position state.direction gives the travel v

Returns an AgentState object for pacman (in game.py) state.pos gives the current position state.direction gives the travel v

Returns possible actions the agent can take in the given state. Can return the empty list if we are in a term

get the current angle the robot body is rotated off the ground

Returns the list of states visited along the path

Returns successor states, the actions they require, and a cost of 1. As noted in search.py: For a given state, this sho

Get an action from the user (rather than the agent). Used for debugging and lecture demos.

Returns a Grid of boolean wall indicator variables. Grids can be accessed via list notation, so to check if there is a wall

Grades each question gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])

Grades each question gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])

Grades each question gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])

Grades each question gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])

Creates an initial game state from a layout array (see layout.py).

Creates an initial game state from a layout array (see layout.py).

Creates an initial game state from a layout array (see layout.py).

Creates an initial game state from a layout array (see layout.py).

Initialize beliefs to a uniform distribution over all legal positions.

Creates an initial game state from a layout array (see layout.py).

Set the belief state to a uniform prior belief over all positions.

Initialize a list of particles. Use self.numParticles for the number of particles. Use self.legalPositions for the legal board positi

Returns true if the stack is empty

Returns true if the queue is empty

Checks to see if the puzzle is in its goal state. ------------- | | 1 | 2 | -------------