Description
1 Introduction
The goal of this assignment will be to implement a working solver for the puzzle game Sokoban shown in
Figure 1. Sokoban is a puzzle game in which a warehouse robot must push boxes into storage spaces. The
rules hold that only one box can be moved at a time, that boxes can only be pushed by the robot and not
pulled, and that neither robots nor boxes can pass through obstacles (walls or other boxes). In addition,
the robot cannot push more than one box, i.e., if there are two boxes in a row, the robot cannot push them.
The game is over when all the boxes are in their storage spots.
In our version of Sokoban the rules are slightly more complicated, as there may be restrictions on which
storage spaces are allowed for each box.
Sokoban can be played online at https://www.sokobanonline.com/play. We recommend that you
familiarize yourself with the rules and objective of the game before proceeding. In particular, lessons #2-1
through #2-15 deal with the restriction variant that we study. It is worth noting that the version that is
Assignment 1, University of Toronto, CSC384 – Intro to AI, Winter 2017 3
presented online is only an example, and that we will give a formal description of the puzzle in the next
section.
2 Description of Sokoban
Sokoban has the following formal description. Note that our version differs from the standard one. Read
the description carefully.
• The puzzle is played on a board that is a grid board with N squares in the x-dimension and M squares
in the y-dimension.
• Each state contains the x and y coordinates for the robot, the boxes, the storage points, and the
obstacles.
• From each state, the robot can move Up, Down, Left, or Right. If a robot moves to the location of
a box, the box will move one square in the same direction. Boxes and robots cannot pass through
walls or obstacles, however. The robot cannot push more than one box at a time; if two boxes are in
succession the robot will not be able to move them. Movements that cause a box to move more than
one unit of the grid are also illegal.
• Each movement is of equal cost. Whether or not the robot is pushing an object does not change the
cost.
• The goal is achieved when each box is located in a storage area on the grid.
Ideally, we will want the robot to organize everything before the supervisor arrives. This means that with
each problem instance, you will be given a computation time constraint. You must attempt to provide
some legal solution to the problem (i.e., a plan) within this constraint. Better plans will be plans that are
shorter, i.e. that require fewer operators to complete.
Your goal is to implement an anytime algorithm for this problem: one that generates better solutions (i.e.,
shorter plans) the more computation time it is given.
3 Code You Have Been Provided
You have been provided:
1. search.py
2. sokoban.py
3. solution.py
4. test script.py
5. tips.txt
Assignment 1, University of Toronto, CSC384 – Intro to AI, Winter 2017 4
The only files you will submit are solution.py and tips.txt. We consider the other files to be starter
code, and we will test your code using the original versions of those files. In order for your solution.py
to be compatible with our starter code, you should not modify the starter code. In addition, you should not
modify the functions defined in the starter code files from within solution.py.
The file search.py, which is available from the website, provides a generic search engine framework
and code to perform several different search routines. This code will serve as a base for your Sokoban
solver. You may also use this code later in the course for your project assignment if you like. A brief
description of the functionality of search.py follows. The code itself is documented and worth reading.
• An object of class StateSpace represents a node in the state space of a generic search problem. The
base class defines a fixed interface that is used by the SearchEngine class to perform search in that
state space.
For the Sokoban problem, we will define a concrete sub-class that inherits from StateSpace. This
concrete sub-class will inherit some of the “utility” methods that are implemented in the base class.
Each StateSpace object s has the following key attributes:
– s.gval: the g value of that node, i.e., the cost of getting to that state.
– s.parent: the parent StateSpace object of s, i.e., the StateSpace object that has s as a successor. This will be None if s is the initial state.
– s.action: a string that contains that name of the action that was applied to s.parent to generate
s. Will be “START” if s is the initial state.
• An object of class SearchEngine se runs the search procedure. A SearchEngine object is initialized
with a search strategy (‘depth first’, ‘breadth first’, ‘best first’, ‘a star’, or ‘custom’) and a cycle
checking level (‘none’, ‘path’, or ‘full’).
Note that SearchEngine depends on two auxiliary classes:
– An object of class sNode sn which represents a node in the search space. Each object sn
contains a StateSpace object and additional details: hval, i.e., the heuristic function value of
that state and gval, i.e. the cost to arrive at that node from the initial state. An f val f n and
weight are tied to search nodes during the execution of a search, where applicable.
– An object of class Open is used to represent the search frontier. The search frontier will be
organized in the way that is appropriate for a given search strategy.
When a SearchEngine’s search strategy is set to ‘custom’, you will have to specify the way that f
values of nodes are calculated; these values will structure the order of the nodes that are expanded
during your search.
Once a SearchEngine object has been instantiated, you can set up a specific search with:
init search(initial state,goal f n,heur f n, f val f n)
and execute that search with
search(timebound, costbound)
The arguments are as follows:
– initial state will be an object of type StateSpace; it is your start state.
Assignment 1, University of Toronto, CSC384 – Intro to AI, Winter 2017 5
– goal f n(s) is a function which returns True if a given state s is a goal state and False otherwise.
– heur f n(s) is a function that returns a heuristic value for state s. This function will only be used
if your search engine has been instantiated to be a heuristic search (e.g., best first).
– timebound is a bound on the amount of time your code will execute the search. Once the run
time exceeds the time bound, the search will stop; if no solution has been found, the search will
return False.
– f val f n(sNode) defines f values for states. This function will only be used by your search
engine if it has been instantiated to execute a ‘custom’ search. Note that this function takes in
an sNode and that an sNode contains not only a state but additional measures of the state (e.g.,
a gval). It will use the variables that are provided to arrive at an f value calculation for the state
contained in the sNode.
– costbound is an optional bound on the cost of each state s that is explored. costbound should be
a 3-tuple (g bound,h bound,g plus h bound). If a node’s g val > g bound, h val > h bound,
or g val +h val > g plus h bound, that node will not be expanded. You will use costbound to
implement pruning in both of the anytime searches described below.
For this assignment we have also provided sokoban.py, which specializes StateSpace for the Sokoban
problem. You will therefore not need to encode representations of Sokoban states or the successor
function for Sokoban! These have been provided to you so that you can focus on implementing good
search heuristics and anytime algorithms.
The file sokoban.py contains:
• An object of class SokobanState, which is a StateSpace with these additional key attributes:
– s.width: the width of the Sokoban board
– s.height: the height of the Sokoban board
– s.robot: positions for the robot: a tuple (x, y), that denotes the robot’s x and y position.
– s.boxes: positions for each box as keys of a dictionary. Each position is an (x, y) tuple. The
value of each key in the index for that box’s restrictions (see below).
– s.storage: positions for each storage bin that is on the board (also (x, y) tuples).
– s.obstacles: locations of all of the obstacles (i.e. walls) on the board. Obstacles, like robots and
boxes, are also tuples of (x, y) coordinates.
– s.restrictions: which storage space coordinates are allowed for each box. s.restrictions[i] is a
list of (x, y) coordinates that the box with value i is allowed to be stored in.
• SokobanState also contains the following key functions:
– successors(): This function generates a list of SokobanStates that are successors to a given
SokobanState. Each state will be annotated by the action that was used to arrive at the SokobanState
∈ {“up”, “down”, “le ft”, “right”}.
– hashable state(): This is a function that calculates a unique index to represents a particular
SokobanState. It is used to facilitate path and cycle checking.
Assignment 1, University of Toronto, CSC384 – Intro to AI, Winter 2017 6
– print state(): This function prints a SokobanState to stdout.
Note that SokobanState depends on one auxiliary class:
– An object of class Direction, which is used to define the directions that the robot can move
and the effect of this movement.
Also note that sokoban.py contains a set of 10 initial states for Sokoban problems, which are stored in
the tuple PROBLEMS. You can use these states to test your implementations. Additional testing instances
will be provided with the evaluation details.
sokoban.py comes with an ASCII visualizer for Sokoban problems with restrictions. The visualizer uses
colour terminal printing in order to display the states in an intuitive manner. This requires a terminal that
supports basic ANSI colour codes, which should be the case for all standard Unix- or Linux-based
terminals (include Mac OS X), as well as many others. On Windows (e.g., when using PowerShell),
installing the Python library colorama and importing it will convert the ANSI codes into appropriate
system calls. The primary colour scheme paints boxes and compatible storage spaces the same colour. In
addition, uncoloured boxes can go into any storage location and uncoloured storage locations can accept
any box. A more complex colour scheme may be used in the additional evaluation problems (with details
to follow).
There is also a non-colour version of the visualizer that can be used by setting
disable terminal colouring = True at the beginning of the state string function.
The file solution.py contains the methods that need to be implemented.
tips.txt will contain a description of your original heuristic—see below.
The file test script.py runs some tests on your code to give you an indication of how well your
methods perform.
4 Assignment Specifics – Your Tasks
To complete this assignment you must modify solution.py to:
1. Implement a Manhattan distance heuristic (heur manhattan distance(state)). This heuristic will
be used to estimate how many moves a current state is from a goal state. The Manhattan distance
between coordinates(x0, y0) and (x1, y1) is |x0−x1|+|y0−y1|. Your implementation should calculate
the sum of Manhattan distances between each box that has yet to be stored and the storage point
nearest to it that is permitted by the storage restrictions.
Ignore the positions of obstacles in your calculations and assume that many boxes can be stored at
one location.
2. Implement Anytime Greedy Best-First Search (anytime gb f s(initial state,heur f n,timebound)).
Details regarding this algorithm are provided in the next section.
3. Implement Anytime Weighted A* (anytime weighted astar(initial state,heur f n,weight,timebound)).
Details regarding this algorithm are provided in the next section.
Assignment 1, University of Toronto, CSC384 – Intro to AI, Winter 2017 7
Note that your implementation will require you to instantiate a SearchEngine object with a search
strategy that is ‘custom’. You must therefore create an f-value function (f val f unction(sNode,weight))
and remember to provide this when you execute init search.
4. Implement a non-trivial heuristic for Sokoban that improves on the Manhattan distance heuristic
(heur alternate(state)). We will provide a separate evaluation document that specifies the performance we expect from your heuristic.
5. You should give five tips (2 sentences each) as if you were advising someone who was attempting
this problem for this first time on what to do. Write these tips in tips.txt.
Note that when we are testing your code, we will limit each run of your algorithm on teach.cs to 8
seconds. Instances that are not solved within this limit will provide an interesting evaluation metric:
failure rate.
5 Anytime Greedy Best-First Search
Greedy best-first search expands nodes with lowest h(node) first. The solution found by this algorithm
may not be optimal. Anytime greedy-best first search (which is called anytime GBFS in the code)
continues searching after a solution is found in order to improve solution quality. Since we have found a
path to the goal after the first iteration, we can introduce a cost bound for pruning: if node has g(node)
greater than the best path the goal found so far, we can prune it. The algorithm returns either when we
have expanded all non-pruned nodes, in which case the best solution found by the algorithm is the optimal
solution, or when it runs out of time. We prune based on the g-value of the node only because greedy
best-first search is not necessarily run with an admissible heuristic.
Record the time when anytime gb f s is called with os.times()[0]. Each time you call search, you
should update the time bound with the remaining allowed time. The automarking script will confirm that
your algorithm obeys the specified time bound.
6 Anytime Weighted A*
Instead of A*’s regular node-valuation formula f(node) = g(node) +h(node), Weighted A* introduces a
weighted formula:
f(node) = g(node) +w∗ h(node)
where g(node) is the cost of the path to node, h(node) the estimated cost of getting from node to the goal,
and w ≥ 1 is a bias towards states that are closer to the goal. Theoretically, the smaller w is, the better the
first solution found will be (i.e., the closer to the optimal solution it will be … why??). However, different
values of w will require different computation times.
Since the solution that is found by Weighted A* may not be optimal when w > 1, we can keep searching
after we have found a solution. Anytime Weighted A* continues to search until either there are no nodes
left to expand (and our best solution is the optimal one) or it runs out of time. Since we have found a path
Assignment 1, University of Toronto, CSC384 – Intro to AI, Winter 2017 8
to the goal after the first search iteration, we can introduce a cost bound for pruning: if node has a
g(node) +h(node) value greater than the best path to the goal found so far, we can prune it.
When you are passing in f val f unction to init search for this problem, you will need to have specified
the weight for f val f unction. You can do this by wrapping the f val f unction(sN,weight) you have
written in an anonymous function, i.e.,
wrapped fval function = (lambda sN: fval function(sN, weight))
GOOD LUCK!
