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Graph Search

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Table of Contents

1) Introduction§

In last week's readings, we looked at two related problems: flood fill (an operation on images for recoloring a contiguous region of similarly colored cells) and path finding (finding and returning a path through a maze, which we arrived at by modifying flood fill to keep track of additional information). In this reading, we'll be building on some of those ideas and formalizing them a little bit, looking at an interesting category of algorithms called graph-search algorithms, which can be used to solve a wide variety of different problems.

These readings will start at a high level, introducing and discussing graph search in the abstract; but by the end, we will be talking in detail about code for graph search and about some of the work involved in bringing these approaches to bear on new problems.

2) What is a Graph?§

Graph-search algorithms are all about systematically exploring structures called graphs; but before we can talk too much about exploring graphs, we will need to define what a graph is. Graphs are structures that represent sets of objects and relationships between pairs of those objects. In 6.101, we'll talk about graphs as consisting of two things in an abstract sense:

  • a set of vertices, one for each object we're interested in; and
  • a set of edges, which represent relationships between those objects.

We will often represent graphs using pictures, like so:

In this kind of drawing, the circles represent vertices in our graph, and each corresponds to an object (with which we label each vertex). Here, we have six objects (A, B, C, D, E, and F), each of which is represented by a single vertex.

The lines that connect the circles in the drawing represent edges, each of which corresponds to a relationship between a pair of objects. For example, B and C are related in some way according to this graph (since there is an edge connecting them), but C and D are not related in that way. In this drawing, each line represents a bidirectional edge: a symmetric relationship between nodes; if the relationship only goes one way, we will draw it as an arrow (a directed edge or unidirectional edge) instead.

That's still quite abstract for now, but that's our notion of a graph. Our focus in this reading will be on a specific category of algorithms called graph search, which involve systematically "exploring" a graph in some way, such that we can answer questions about it. There are a number of questions that we might be interested to ask about a graph, for example:

  • What are all of the objects that are directly connected to A?
  • What are all of the objects that I can reach by traversing exactly two connections?
  • What if I am willing to traverse an arbitrary number of connections?

All of these questions can be solved using graph-search approaches, but our focus in 6.101 is going to be on a particular subcategory of graph-search algorithms, path-finding algorithms, which we will use to answer the question: given a starting object and a goal object, what is a path that connects the two?

If you recall last week's reading, this idea is very similar to the last problem we tried to solve, where we were trying to find a path from one location in a maze to another. This is the same kind of idea, but rather than focusing on images in particular, we're generalizing this notion to finding a path in a graph: a sequence of vertices (or a sequence of edges) leading from our starting vertex to our goal vertex.

2.1) Uses§

So far, this might all seem very abstract. But the idea of a graph, and the algorithms we're going to talk about today, are not just interesting made-up things to think about (although they are kind of interesting in their own right); these ideas have tremendous practical utility in a broad variety of contexts. Lots of real-world domains can be thought of in terms of objects and their relationships to each other; this allows us to construct graphs corresponding to these domains, and graph-search approaches allow us to answer questions about those contexts. So it turns out that a wide variety of problems can be solved by: constructing a graph related to that problem, using some sort of graph-search approach to answer some questions about that graph, and then reinterpreting those answers in terms of the original problem that we started with. There are a large number of examples, but to name a few:

  • Web search involves relating web pages to each other and ranking them relative to each other. One of the early approaches to ranking search results, PageRank, ranks pages using a graph whose vertices represent individual web pages and whose edges represent hyperlinks between them.

  • Robot navigation is about planning actions that a robot can take to affect the world in some way (move to a different location, bake a cake, deliver a package, etc.). Determining what actions the robot should take to accomplish its goal often involves exploring a graph where vertices represent possible states of the world, and edges reflect actions that the robot can take.

  • VLSI circuit layout involves arranging millions of transitors on a chip and connecting those components with wires, subject to constraints on the physical locations of the various components (which may affect each other), the total area of the circuit, the total length of wire, or other constraints. This is a complicated process, but it is often solved using graph-search techniques on multiple graphs related to the components and the connections between them.

  • Social-network graphs represent connections between different people (or other entities), based on relationships or past communications. Exploring these graphs can answer complicated questions about these relationships, such as finding groups of people connected by common interests for purposes of targeted advertising, discovering new relationships based on other connections, and more.

  • Route planners like the GPS-powered systems in most modern cars find optimal paths between physical locations using common modes of transport. These systems often work using graphs of location data (locations connected by ways to move between them) and finding paths between locations that are optimal in some sense (minimizing travel time, optionally accounting for tolls and traffic, etc.).

  • Many kinds of puzzles and games can be solved using graph-search techniques, often by constructing a graph where each vertex represents the state of the game at a certain point and the edges represent moves that players can make. By finding paths through those graphs, we can often figure out the optimal (or nearly optimal) move to make based on the current state of the game. These techniques are often used when a game is played against an artificial intelligence or when games otherwise contain agents that are not controlled by human players.

This is just a small subset of the kinds of problems that can be solved using graph search, but hopefully it gives a sense of the power of some of these approaches. It's also worth tempering expectations a little bit; many of the specific problems mentioned here involve humongous graphs and require more advanced techniques than we're going to be able to cover in 6.101 (we'll focus our attention on a small subset of graph-search algorithms). That said, the techniques will serve as an introduction to graph-search techniques and as a foundation on which you can build later on if you're interested.

2.2) Another Example: The 15 Puzzle§

Let's take a look at a concrete example of a puzzle that can be solved using graph search: the 15 puzzle. The 15 puzzle consists of a board split into a 4-by-4 grid, with each cell except one occupied by a numbered tile. The goal is to get the board from an initial scrambled state (example shown on the left below) into an ordered state (shown on the right below) by repeatedly sliding tiles into the open position. From each board position, we have between two and four options for moves; for example, considering the board on the left, we have three options for how to proceed: we could move the "5" tile down into the open space, the "12" tile to the right into the open space, or the "7" tile up into the open space.

The reason to mention this puzzle, aside from it just being kind of a fun puzzle, is that it is one example of a kind of problem we will be able to solve by finding a path through a related graph. Consider creating a graph where each vertex represents the state of the board, and these vertices are connected by edges representing moves that we can take to move between the states. Graphically, we might draw such a graph like so (here only showing a small portion of the graph):

Notice that each vertex here corresponds to one possible state that the game board could be in (an arrangement of the tiles) and that the edges connecting the vertices represent moves that we could make (tiles that we could slide) to move between the states. Now, we can imagine that if we were to draw out the full graph, somewhere in that graph would be a state corresponding to our solved puzzle, and a path (a sequence of game states) starting from our original configuration and ending with the goal configuration would represent a solution to the puzzle and tell us what moves we would need to make in order to solve it:

Before we take a look at some results, let's think a little bit about how big of a problem this is. How many possible distinct arrangements of the tiles are there on the board?

We'll return to this specific example a little later in this reading, but for now, let's look at one solution to the problem (you can use the buttons below to step through it):

Explanations go here.

This solution consisted of 42 board states (41 moves). Given this result, and given the scale of the problem, there are some questions we might ask ourselves:

  • Is this solution optimal? That is, is there a solution out there that requires fewer than 41 moves?
  • How many of the ~10 trillion possible board states do I need to consider in order to find a solution? Could we do things more efficiently?

For the rest of these readings, we're going to build on these ideas by building on the things we started talking about in last week's readings and formalizing some of those ideas to develop general-purpose path-finding algorithms, and then we'll also spend a little bit of time thinking about how those algorithms perform.

3) Pathfinding in the Abstract§

Our approach to pathfinding will be very similar to our code for flood fill from last week. In that example, we maintained an "agenda" (or "work queue") of pixels that we eventually needed to color in, and then we repeatedly pulled one pixel off of the agenda, colored it in, and then added its children back to the agenda. Here, we will do something similar, but our agenda will consist of paths from some starting state to some other state. But we'll still work our way through this process in the same way: we'll repeatedly pull a path out of the agenda, consider it, and add its children (new, longer paths!) onto the agenda. Here is our approach outlined in pseudocode:

To find a path from a state t to some other state:

  • Initialize an agenda (list of paths to consider) containing a single path that consists of only our starting state

  • Initialize a visited set (all states that have ever been added to the agenda) to contain only the starting state

  • Repeat the following:

    • Remove one path (t\to s_1\to s_2\to \ldots \to s_N) from the agenda
    • For each "neighbor" state n of s_N (for each state n directly connected to s_N via an edge):
      • If n is in the visited set, skip it
      • Otherwise, if n satisfies our goal condition, return the path (t\to s_1\to s_2\to \ldots \to s_N \to n)
      • Otherwise, add n to the visited set and add the path (t\to s_1\to s_2\to \ldots \to s_N \to n) to the agenda

    until either we find a path or the agenda is empty (in which case no path exists)

So there's our high-level approach, and it maybe doesn't sound like much: we're going to keep track of all the paths we need to consider, then consider them one after the other until we find one that satisfies the goal condition we're looking for. But, while this approach doesn't sound too impressive on the face of it ("just keep looking at new paths until you find one that works"), it's taking advantage of two useful properties of computers: they're good at doing a lot of things really quickly, and they're good at remembering lots of things. Putting that all together, these ideas will end up giving rise to programs that feel quite smart and that can compute some impressive results.

3.1) Order Matters!§

The approach outlined above is almost complete, but it turns out that one key piece of information is missing.

As we work through this process, we'll generally have multiple paths in the agenda (often, a whole lot of paths!). But the first step in the loop in our algorithm simply says to "remove one path from the agenda;" it says nothing about which path we should remove! And, similarly, it talks about "adding [new paths] to the agenda," but it doesn't say anything about where we should put those new paths!

It turns out that the order in which we consider the paths has a big effect on the way that this approach "explores" the graph it's given and also on the nature of the paths that we ultimately return. So it will be worth taking some time to explore these effects.

Let's see how this process plays out on a small example graph:

How many vertices does this graph contain?

How many edges does this graph contain?

Let's now consider the process of finding a path from A to I in this graph, using the approach we outlined above, and we'll try it with a few different rules about ordering. All of these processes are going to start the same way: our agenda will contain a single path (the path containing only A); then we will remove that path and add its children (two paths, one representing AB and one representing AD). After that point, we will start to see the differences that arise from different choices about the order in which we consider the paths from the agenda.

To help us with this, we'll visualize things in a slightly different way, using a structure called a "rooted tree," which we'll use to try to represent all possible paths through the little graph from above. Here is an example showing the paths starting at A (note that we're only showing a portion of the whole tree):

Unlike our original graph drawing, the circles here represent entire paths starting at A; and we explicitly diagram the idea that paths are sequences of states, and, in theory, a path can contain the same state more than once.

3.1.1) Example One: Replace First Path With Its Children§

Let's start by considering the following rule for our agenda: each time we want to remove a path from the agenda, we will remove the element from the front of the agenda; and when we add neighbors on to the agenda, we'll also add them to the front of the agenda.

The example below shows the start of how this process evolves when searching from A to I in the graph above.

Use the buttons below to step through this example, paying careful attention to how the agenda and the visited set evolve, as well as the order in which we're considering paths. In these graphs, we will color paths that have been added to the agenda but not yet considered in blue; and the path currently being considered will be shown in red.

Explanations go here.

What path from A to I will ultimately be found by this approach when it finishes? Enter your answer as a sequence of state names with no punctuation, e.g. DCBA:

But let's think not only about the solution here; let's look also at the way in which this algorithm explored the graph. This approach seemed to be going all the way down one path as far as it could, before coming back to consider other paths. Graphically, this approach is growing a single path as deeply as it can before going back and looking at other paths. This idea (of exploring the entire depth of the tree before really considering any of the breadth of the tree) is referred to as a depth-first search (DFS for short).

3.1.2) Example Two: Replace Last Path With Its Children§

Now let's take a look at another approach. Here, we are again looking for a path from A to I, but instead of replacing the first (left-most) path in the agenda with its children, we're instead going to replace the last (right-most) path in the agenda with its children. Before clicking through the examples below, try walking through this example on paper in its entirety. You don't need to draw the whole tree structure, but try to work through how the agenda and the visited set will evolve as we perform this search, as well as the path we ultimately return using this approach. Once you have an idea of what you're expecting, click through the example below to verify:

Explanations go here.

What path will ultimately be found by this approach? Enter your answer as a sequence of state names with no punctuation, e.g. DCBA:

How can we characterize this approach? Well, this one, too, is going down a single path as far as it can, exploring the entire depth of the tree before coming back to explore any of its breadth; so despite the change, this is still a depth-first search, it just happens to explore the right side of our tree instead of the left.

3.1.3) Example Three: Remove First Path, Add Children to End§

Now, one more time, lets look for a path from A to I, but let's consider yet another different kind of ordering we could try: let's try removing paths from the front of the agenda but adding children to the end. Once again, try to work through this example on your own before using our answers below as a self check.

Explanations go here.

What path will ultimately be found by this approach? Enter your answer as a sequence of state names with no punctuation, e.g. DCBA:

Once again, we cut off the example a little bit before the end, but hopefully we can see that this approach is characteristically different from the first two orderings we tried. This approach is called breadth-first search (or BFS for short) because we are exploring the entire breadth of the tree before considering paths deeper down the tree. Said another way, this approach considers all paths of a given length n before considering any paths of length n+1.

3.1.4) Order Matters: Summary§

In this section, we've looked at a couple of very slight variations of the algorithm we presented above. The only thing we changed was how we decide which path to consider next when there are multiple paths on our agenda. But this slight difference in ordering had a big effect on the way in which our algorithm explored the graph; and, ultimately, it will have a big effect on the kinds of paths we return in the end.

The big takeaway here is that if we always consider the newest path that was added to the agenda (i.e., the path that was added to the agenda most recently), we end up performing a depth-first search. If we instead consider the oldest path (i.e., the one that was added earliest), we instead perform a breadth-first search.

3.2) Another Example§

In the examples above, we saw these differences play out in a lot of detail. Next, let's take a step back and look at a related example to see if we can get more of a sense of how DFS and BFS differ in terms of the way they explore a graph. Watch the following brief video for another example:


3.3) Check Yourself: USA§

In the video below, each light-grey dot represents an intersection of two or more highways in the United States. The video shows various paths as they are considered as part of planning a path from Smith Center, Kansas (the geographic center of the US) to Cambridge, MA. Watch the video below to answer: which kind of search is being performed here?

3.4) Check Yourself: Back to Flood Fill§

We can also see these kinds of differences play out in our flood-fill example from last week. Even though we weren't finding paths to start with last week, we were still keeping an agenda of pixels that we eventually wanted to color in, and by changing the order in which we consider elements from the agenda, we can affect the order in which we color the pixels in. This doesn't affect the end result of the flood fill, but it does affect the intermediate results.

Consider the following three videos, each of which shows the evolution of a flood fill process:

Method 1Method 2Method 3

One of the flood fill implementations used a breadth-first approach: the pixel we chose to color in at any given point was the one that had been in the agenda the longest. Which of the videos corresponds to this approach?

One of the flood fill implementations used a depth-first approach: the pixel we chose to color in at any given point was the one that had been added to the agenda most recently. Which of the videos corresponds to this approach?

One of the flood fill implementations used a different approach: choosing a pixel from the agenda at random each time. Which of the videos corresponds to this approach?

4) Summary of BFS vs DFS§

So far, we have been thinking fairly abstractly, but we have introduced the notion of graph search, with a specific focus on pathfinding. And we've also seen several examples of the difference between breadth-first and depth-first approaches. Before we move on to turning these abstract ideas into working Python code, let's take a moment to discuss some of the differences between BFS and DFS when applied to path finding.


  • To implement a breadth-first search, we add and remove elements from opposite sides of the agenda. This approach is known as "first-in, first-out" (commonly written/pronounced as "FIFO") since the element we remove is always the one that was added first.

  • BFS is guaranteed to return a shortest path to a goal vertex if such a path exists, regardless of the structure of our graph. Because we consider all paths of length n before considering any paths of length n+1, we know that the first time we encounter a state that satisfies our goal condition, the path we're considering must be optimal (in the sense that there is no shorter path to the goal).

  • BFS can run forever if it is being applied to an infinite graph with no solution, but it will always terminate in a finite graph or in an infinite graph where a solution exists.


  • To implement a depth-first search, we add and remove elements from the same side of the agenda. This approach is known as "last-in, first-out" (commonly written/pronounced as "LIFO") since the element we remove is always the last one that was added.

  • DFS is guaranteed to find a path to the goal (but not necessarily an optimal one) if such a path exists and if the graph is finite.

  • DFS may run forever on an infinite graph even if a solution exists.

4.1) DFS = Bad?§

In all of the examples we have seen so far, DFS gives us a pretty gnarly path in the end, something that is kind of ugly and far from optimal. And in the section above, we've stated that the guarantees that DFS offers us are weaker than those that BFS offers us. So at this point, you may be thinking: why did we even bother introducing DFS? When would you ever want to use it?

The answer is a little bit nuanced, but the short version is that DFS tends to use less memory than BFS. If we consider a graph with a "branching factor" of b (i.e., every state is connected to b other states), then there will be around b^n paths of length n. This means that there will be around b^n elements in the agenda when we're considering the last path of length n-1. By contrast, the agenda for a DFS will have around b\times n elements in the agenda when considering a path of length in; and as b and n increase, b\times n is substantially smaller than b^n. This is especially useful in cases where we don't care about finding the optimal path to a state (sometimes any path will do, or sometimes we're just looking for a particular state and don't care about the path at all!), and in those cases, DFS can sometimes be the right choice.

5) Pathfinding in Python§

Now that we've spent some time thinking about graph search as an abstract process, let's think about how to turn it into Python code so that we can realize the power of these techniques.

As always, when we think about turning an abstract idea into something concrete in Python, an important question has to do with our choice of data structures. That is to say, we have several things that we're going to need to represent in Python, as well as operations that we'll need to perform on those things. In some sense, we have infinitely many choices for how to represent each of those things concretely within our program, but as we've seen in earlier readings and recitations, the data structures we choose to represent those things can have a dramatic effect on the code that we end up writing in terms of clarity, the ease with which we can avoid common bugs, and efficiency. So lets spend some time here thinking about representation. In particular, there are at least 4 distinct things we need to be able to represent in order to write code to solve paths using the approach outlined above:

  • the graph itself (the various states and the connections between them)
  • the start state and goal condition
  • a candidate path (a sequence of states)
  • the agenda
  • the visited set
Check Yourself:

Take a moment to think about different ways that we could represent each of these things. There are multiple options for each; what are some of the tradeoffs?


We'll revisit some of these choices a little bit further down the page, but for now, here's one option we can go with:

  • The graph needs to represent all of the available states as well as the connections to them, and we need to be able to quickly answer the question of which states are neighbors of a given state. For this purpose, we can use an "adjacency dictionary." Each key in our dictionary will be a single state, and the associated value will be a list of the states that share an edge with that state. This representation affords us an efficient way to look up the neighbors of any given state, which is an important action that we'll repeat quite a lot.

  • We'll assume a well-defined start position. The exact form of this will depend on the problem we're trying to solve, but we will assume that our starting state is a state in whatever graph we're searching over.

  • The goal condition is a little more complicated to think about. It is tempting to say that it should also just be a state in our graph. We're going to use a slightly more general representation, though: a function which takes a state as input and returns a Boolean value indicating whether that state satisfies our goal condition or not. This will allow us to use a single search to find paths through a graph where any of a number of states are OK as goals (for example, we might want to ask our GPS navigation system to take us to "any gas station" rather than to a particular one).

  • A path consists of an ordered sequence of states. This limits our choice of representation somewhat, but in our code moving forward, we will represent a path as a tuple containing the states in the path, in order (with index 0 corresponding to our starting state). The form of the states themselves will vary based on the problem we're solving, but we'll always represent a path as a tuple containing one or more states.

  • Our agenda needs to store elements in a given order, and we'll need to be able to remove elements from either side of it. The fact that the agenda needs to maintain order suggests that either a list or a tuple would be appropriate; and because we're going to need to be adding and removing things from it, we'll go with the mutable option: a list.

  • Our visited set does not need to maintain order, and the only thing we really do with it is add elements to it and check whether elements are already in it. As we've seen in recitation, if we were to use a list for this, the amount of time that that membership test would take would grow roughly linearly with the length of the list; a Python set is a great choice so that we can do that check efficiently. But we'll need to be careful to remember that we can only add hashable elements to it.

Check Yourself:

With those decisions in mind, try your hand at writing this function. Note that the structure is very similar to what we saw with flood fill last week, so you may wish to use that as inspiration.

def find_path(graph, start, goal_test):
    if goal_test(start):
        return (start, )

    agenda = [(start, )]
    visited = {start}

    while agenda:
        this_path = agenda.pop(0)
        terminal_state = this_path[-1]

        for neighbor in graph.get(terminal_state, []):
            if neighbor not in visited:
                new_path = this_path + (neighbor,)

                if goal_test(neighbor):
                    return new_path


Double-check that every line of code here makes sense to you. If not, please reach out and ask for help (during open lab hours, instructor office hours, or via the 6.101-help@mit.edu mailing list).

6) A Small Example§

With this code in hand, let's walk through a few example programs. Here is a small, contrived example graph that we can start with:

Given the choice of representation we discussed above, how would we represent this graph in Python? Store your answer in a variable called test_graph. Note that the states here are integers (and should be represented by Python int objects), and note also that the edges in this graph are directed (one-way), and make sure that your representation accurately reflects this.

Try to answer the questions below without using a Python interpreter.

Consider using the find_path function from above exactly as it is written. How many states will be in the path we return from calling find_path(test_graph, 13, lambda state: state == -32)?

What would happen if we instead called find_path(test_graph, 13, lambda state: -32)? If the function would return a value, type the resulting value in the box below. If it would produce an exception, write error. If it would enter an infinite loop, write infinite.

What would happen if we instead called find_path(test_graph, 13, -32)? If the function would return a value, type the resulting value in the box below. If it would produce an exception, write error. If it would enter an infinite loop, write infinite.

What would happen if we instead called find_path(test_graph, 0, lambda x: x == 215)? If the function would return a value, type the resulting value in the box below. If it would produce an exception, write error. If it would enter an infinite loop, write infinite.

In the example from the previous question, find_path(test_graph, 0, lambda x: x == 215), what elements were in the visited set by the end of the function call? Enter your result as a Python set below.

Check Yourself:

Even though this function currently implements a BFS, it is possible to change a single line such that the function performs a DFS instead. What line could we change to make that happen, and what would we need to change it to?


In order to change this code to a DFS, we need to set things up so that we are removing paths from the same side of the agenda we're adding them to. The lines that govern this behavior are line 9 and line 19. And indeed, changing either of those lines can work. We could either change line 9 to say this_path = agenda.pop(-1) (or even this_path = agenda.pop()), or we could change line 19 to say agenda.insert(0, new_path).

In general, the change to line 9 should be preferred for efficiency reasons (adding and removing from the end of a list is generally more efficient than adding/removing from the beginning of a list).

7) Revisiting Representation of Graph: 15-Puzzle§

Let's return now to a problem we discussed earlier in the reading and try to bring this code to bear on the 15-puzzle. To start with, any time we are thinking about using search to solve a new problem, an important question is: how do I represent the states (the vertex labels) for this problem such that my code can be made to work with it. So let's start there.

Check Yourself:

Take a moment and think about how you might represent the states in this graph, keeping in mind that, in the graph we drew all the way back in section 2.1, each vertex was labeled with a representation of a possible layout of the game board.


We have many options here, but the code in find_path limits us somewhat. Our choice of state representation is used in two places in the code: one is that each path is represented as a sequence of states; the other is that we store states inside of our visited set. So unless we want to change our visited set into a visited list (why might we not want to do that?), we are limited to hashable representations of the game board.

We still have options here, but when we note that the board is a collection of elements and that the order of those elements matters, we might lean toward using tuples for our representation. This is not the only option (and there are multiple ways to use tuples), but we could imagine using a 2-d array of numbers (stored as tuples of tuples) to represent the board.

Even once we have settled on that choice, there is still the question of: how do we represent the empty square? The most important feature that this representation needs to have is that it should be impossible to mistake it for a regular spot occupied by a tile. There are options here. For example, any non-integer would work, or maybe a negative integer. But it is often nice to have this representation really stand out; a good choice in this case might be to put None in the place representing the location of the empty tile. So an example board using this representation might look like:

((2, 6, 3, 15), (11, 9, 4, 5), (1, 8, 12, None), (13, 14, 10, 7))

From here, it's worth asking ourselves: can we imagine using our find_path function from above to solve this puzzle? Here we're confronted with a limitation of an early design choice: for the 15-puzzle, generating the dictionary to represent the graph would take a long time, and, even worse, the resulting structure would be far too big to fit in memory! It doesn't make sense to spend the time building up that representation just to then go back through the whole structure looking for a path. What we'll look at next is an approach to making this whole process more feasible.

Before we dig in, it's worth mentioning that this kind of revision (which often requires deleting and rewriting code!) is completely normal. It takes a lot of practice to make informed decisions about these kinds of choices, and even with that experience, it's common not to notice until later than we should have structured things differently. So the process of going back and changing code to use a better internal representation is not an uncommon occurrence.

Check Yourself:

A place to start when thinking about different ways to represent graphs is to look at our search code and ask ourselves: what questions are we asking about the input graph?


The only place we're using graph in our search code is to look up the neighbors of a given state. And what's more, in many graph-search problems, we probably won't end up asking for the neighbors of the majority of states in the graph. So now we can ask ourselves: can we think of a different structure that we could use to answer that same question, but which doesn't require precomputing the entire graph structure?


This might seem a little bit strange, but a great choice here is a function! If we can define a function that takes a state as input and returns a list of directly connected states, we can use that function to answer the question we need to answer by changing line 12 in find_path so that instead of looking up the terminal_state in a dictionary to find the list of neighbors, we instead call a function to find those neighbors. We will also need to change our function signature (and docstring if you've written one), so that it is clear that our input representing the graph is now a function (along those lines, graph was not a great name for that variable to begin with; perhaps graph_dict would have been better, and a different name probably makes sense after this change).

The beauty here is that this allows us to represent the structure of the graph without storing an explicit representation of every vertex and every edge in the graph; we discover and store information about the relevant connections in the graph only when our search process comes across them, which saves us the trouble of constructing the whole graph and also helps us avoid wasting memory on parts of the graph that our search process might never explore.

Check Yourself:

Now, try your hand at making the necessary changes, before looking at our code below.

Show/Hide Our Code

Here is our code, with the modified lines highlighted in yellow. This is a small change, but there is a lot of power in it!

def find_path(neighbors_function, start, goal_test):
    if goal_test(start):
        return (start, )

    agenda = [(start, )]
    visited = {start}

    while agenda:
        this_path = agenda.pop(0)
        terminal_state = this_path[-1]

        for neighbor in neighbors_function(terminal_state):
            if neighbor not in visited:
                new_path = this_path + (neighbor,)

                if goal_test(neighbor):
                    return new_path


This change allows us to solve things like the 15-puzzle, where constructing the whole graph preemptively would be prohibitively expensive. We'll leave the task of writing the code to solve the 15-puzzle as something to do on the weekend if you're bored and looking for something to do; but let's see if we can make use of this new structure to solve a different problem.

8) Another Example: Word Ladders§

Let's use our new function-based path-finding implementation to solve a different kind of puzzle, known as a Word Ladder. In this puzzle, we are given two words, and our task is to find a sequence of words that connect those two words to each other such that everything in the sequence is a word, and each word in the sequence differs from the one before it and the one after it by exactly one letter.

For example, a puzzle might be given as something like the following (it's common for the starting and ending words to be opposites of each other):


And a valid answer to the puzzle is:

Show/Hide Solution

Note that every element in the sequence is a valid word and that each varies from the words preceding and succeeding it by only a single letter.

This problem is a prime candidate for our path-finding approaches, and a natural representation is to use the words themselves as states and to have edges connect words that differ only by a single letter. But, like the 15-puzzle, this has a problem. Let's take a look at just a portion of the graph for solving a common puzzle where the starting word is "fool" and the ending word is "sage". Let's start by looking at just the single vertex representing the word "fool":

Isn't it beautiful? But it's not all that exciting. Let's take a look at a slightly larger piece of the graph, showing all of the vertices that are one step away from "fool":

and we can carry things further by looking at all vertices that are up to two steps away:

Argh! And as you can imagine, the graph gets ever bigger and more complex the farther we move away; so this is a great example for testing out our new implementation of find_path! Try your hand at setting up this problem by filling in the code box below, to set up a search from "patties" to "foaming" in the graph we've been talking about.

You can replicate the ALL_WORDS variable on your own machine (if you want to test things out there first) by copying the relevant code from the box below and downloading words.txt to the same directory where you're writing your code.

9) Summary§

Once again, it feels like we have come a long way. We started by thinking back on last week's example problem of flood fill, and then we expanded and formalized it, arriving at the idea of graph search.

Throughout today's reading, we explored several facets of graph-search algorithms. We started by talking about graph search in an abstract sense, including discussing the dramatic differences in behavior that arise from changing the order in which we consider elements in the agenda. We also saw several examples of taking problems from the real world and representing them as graphs.

Which of the following statements are true?

We then moved to implementing a path-finding algorithm in Python, and we spent a lot of time thinking about how to represent different structures related to graph search in Python, eventually ending up at a 20-line implementation of a path-finding algorithm. There is a lot of interesting stuff going on in those 20 lines and a lot of power from relatively little code! We also worked through examples of using that code to solve a few different kinds of problems, and we used those problems as a guide as we sought to make improvements to the code.