If you read a short description of a patern in a usual pattern book or in a Wikipedia article, you will most likely not realize its full power. A true realization comes when you derive the pattern from the programming tasks you are to accomplish on your own, bottom-up.
A month ago I was chatting with a peer who was about to finish his Object-Oriented Programming course. One of the patterns he mentioned was Strategy. As I didn't recognize what it was about at once, I thought that it was fairly useless. Having gotten home and refreshed my memory with this article, I realized that this heuristic, of inferring the lack of use from if I remember what it's about, led me to the right result. The pattern was about encapsulation of a family of algorithms behind an interface. The lack of sense was confirmed by a detailed, but not assuring description of how the Strategy was different from the Bridge pattern.
"Well, just a yet another useless pattern," I had thought... until I found myself writing a bunch of strategies in my script three days later.
The Strategy pattern
First, lets refresh what the strategy pattern is. This is yet another behavioral pattern that supplies the programmer with encapsulation of the behavior. Strategy encapsulates several methods with a common interface, and allows a programmer to switch between strategies seamlessly and reuse them in different environments.
This functionality is not unique. The Interface pattern, Virtual Function pattern, and even simple Function Pointers provide more or less the same. Other unimportant details are only interesting to OOP aficionados. Why bother with Strategies at all then?
There is one case, however, when thinking in terms of Strategies is not artificially induced by OOP courses, but rather is quite a natural way to think about your algorithms. This us when you play a game--or write a computer program that plays one.
There is a whole genre of video games named "Strategies". Though gaming is not the main focus of this post, a lot of what I describe below applies to these games as well. Sadly, in these days of video game consoles, Strategy games do not get as much traction as earlier.
"Still narrow", you might think, "I'm not a game AI programmer, so I'm not going to need this". This argument is relevant indeed, but it.misses the fact that some activities may be modelled as games. This doesn't necessarily refer to gamification, a popular concept in our times of ubiquitous crowdsourcing and Web2.0.
The games I'm talking about are closer to mathematical game concepts, when the environment of your application is modeled as "adversary" who doesn't really tend to respond to your actions in the nicest way possible. An example of that is the script I was implementing. I wanted to create a crawler for a web forum, that should download new posts as soon as possible, given that posts may be deleted by evil moderators, which should not confuse the script. I'll expand on how this was modeled as a "game" later; let's talk about strategies for now.
Let's take a less abstract example of a game to explain what the strategies are about. You a played Battleship, a game where you are to destroy ships on the adversary's board with as few moves as possible. in this game you make a move by shooting to a particular cell of the battlefield, and get a response whether you missed, or hit the ship, or destroyed it completely if it's the case. In fact, you have a "Strategy" that reacts with moves to the messages from the battlefield. (We intentionally leave the art of ship positioning aside).
Implementing a strategy for Battleship game was the very first task I was given as an intern at my first job at ISPRAS. We were tow implement a strategy for the game, and our algorithms competed with each other afterwards. They played thousands of matches, each being a comparison of who was faster to shoot out a randomly generated battlefield. I came up with two strategies: one hacked into the random number generator by srand()-ing it to a predefined value (that won all the matches), and the other was built via a series of experiments with combinations of the strategies implemented following the pattern described in this post.
The programs we wrote back then revealed a lot about our consequent professional lives. One of my peers pre-analyzed the probabilities of a cell being occupied by a ship, and built his strategy on top of this; he's finishing his Ph.D now. The algorithm of the team supervisor was the simplest, the fastest, and had the largest number of victories (naturally). And my algorithm was over-engineered, slower than the others, but flexible and efficient enough: it scored 3rd in the competition.
Most strategies look like this: Shoot the battlefield along diagonals, starting from the middle, until you encounter a hit. As soon as you do, finish th ship you hit by shooting nearby cells, and keep shooting across the diagonals.
Of course, a multitude of other, more complex strategies exist. Each of the strategies, however, fits the common interface: given a result of the previous move, make the next one. The strategy should, thus implement the following interface:
Decomposition of a complex, stateful strategy
A strategy to play even such a simple game may be quite sophisticated. The implementation of next_move will embody a number of states we're currently at (such as "shooting along diagonals", or "trying ti finish a ship".), and the strategy will also implement a complex logic to transition among the states based on the shot result. This pattern, known as "State Machine" is viable, but the whole logic may be simplified.
We can use the rethinked strategy pattern to decompose the strategy into smaller ones. The "global" strategy described above in italics may be turned into a local one as follows. Instead of approaching the way we play a game as one "big" strategy, we instead consider that our strategy changes each move as well. In response to a message from the field, the rethinked next_movefunction now returns the strategy we are now using as well as the next move.
This way, we begin with "shoot diagonals" strategy; it changes to the "kill ship" strategy as soon as the response is "hit", which, in turn, changes back to the shoot diagonals strategy as soon as the response is "ship dead".
A clean way to implement it is to change the interface of a strategy slightly. Instead of a single next_move function that both returns the next move, and changes the internal state, we transform the interface as follows:
The peek function returns the next move you should make according to this strategy, and move function returns the next strategy.
This offers a simpler way to switch strategies. When the strategy feels that a strategy should be switched, it returns NULL from peek, this makes the caller detect that the strategy is going to switch, and doesn't pass the move to the adversary. Instead, it peeks the next strategy. The overall algorithm now looks like this (memory allocation is intentionally put aside):
You may do this in a more complex but more encapsulated manner. In the peek function, you make construct the next strategy, and peek its next move; save the strategy, and then route the move function to it, returning the strategy built.
The implementatinon for the Bttleship game stereos would look like this. Shot diagonals strategy is initialized with a battlefield state, and this should be enough for choosing the next move. As soon as a hit is encountered, the peek function returns nil, and the move func returns "finish the ship" strategy. The "finish ship" strategy is initialized with the last hit, and explores the area around the hit. As soon as the result is "killed", it initializes the diagonal strategy with the current battlefield, and returns it as the next strategy.
Now the implementation looks much more clean and decomposed. You can combine and chain together various strategies, discovering the most efficient through experimenting.
Note that this algorithm doesn't have any protection against infinite loops, so you have to ensure the termination manually. On the other hand, no other way to implement a strategy for a game has any protection against this.
A functional way to encode the strategy change
The decomposition may be continued. We see that the implementation of the "kill ship" strategy has the switch to a specific strategy hardcoded. This limits the reuseability of the class, because we would like to switch to killing a ship and back from many different strategies, rather than just from the diagonal one only. This hardcoding may be solved via subclassing or via using functors, i.e. passing a function to the strategy constructor (I prefer the latter).
When a specialized strategy exhausts the moves it can—or was ought to—make, it calls the function supplied to the constructor (or a virtual method, if you preferred subclassing), and this function returns a new strategy. Most likely, the function is taught to return the previous strategy via a closure. I.e. the "shoot diagonals" strategy's move function may contain such (pseudo)code (I tried to use C++0x lambdas here):
Concurrent execution of strategies
An established interface allows us to define combinations of strategies. Assume we would like to shot along the diagonals in different regions of a battlefield. Instead of implementing a strategy that shoots along two, three, etc regions, we may implement a strategy composition.
A simple composition takes an array of strategies, and asks them for the next move in a round-robin way. In some cases, it's a sufficient composition. In battlefield, however, we don't want, for instance, shoot into the othee regions, if we've hit a ship; what we want is to pay our full attention to that ship. In this case, we would use a priority queue instead to store strategies instead od an array.
What about forum downloading?
Now assume we are to download all messages from a forum, given
- each message has a unique id that automatically increments;
- there's no way to get the current maximum id without posting a new message;
- Moderators may erase messages, and there's no way to distinguish if a message with a particular ID was erased, or it never existed (aside from encountering a message with a greater id);
- we should be capable of both getting new messages and downloading the old messages at the same time
We can model the work of such a crawler as a battlefield-like game. Our "move" consists of an ID of a message to download, and of a pause to make before doing so. Our response is the message contents or a notification the it doesn't exist.
Our "strategy" would be a round-robin combination of two strategies. The first goes down from a specified id down to the message number one. The second waits for the new mesages in the following way.
The strategy goes up message by message until it encounters a nonexistent message. Then the strategy switches to a "random walk" strategy that finds a message with a greater that exists, making appropriate pauses. No matter how many messages a moderator would delete between our attempts we should be capable to finding a message that is not deleted. After we found one, we may be sure that all the nonexistent messages between the last one we downloaded successfully and the one we found during the random walk either exist or are removed forever. This part is implemented by another strategy, which then switches to the "random walk", looping the execution.
I implemented a slightly different downloader, given that we always know the ID of the last message, regardless of whether some latest messages were deleted. You may check its sources here (ahe downloader as at the time of writing), and here is the latest version of the script
Other applications and limitations
The most obvious limitation is that all the strategy classes you implement depend severely on what you're move is. Each strategy class you implement is going to construct move objects. If the interface in the constructor used changes, or its meaning becomes different then you have to alter all the classes you implemented. The same applies to the response objects.
One of the oldest turn-based strategy games, chess, is one of the examples where the approach described is not the best one possible. A more effective way to play chess may be something like Alpha-Beta-pruning rather that structural exploration.
Trying to analyze the possible applications of this strategy framework, I came to a conclusion that it's quite limited. It is a nice way to model a strategy when your game is to explore "terra incognita", to reveal what is initially hidden. Indeed, in Battleship you do not react to intelligent moves of your adversary. Instead, you explore a carefully prepared field, initially covered by the fog of war. The forum downloader is no different: the actions of the evil moderator are in no way affected by the "moves" the program makes.
The framework supports parallelization via clever strategy combination classes, like the one I described earlier. Note that this abstraction moves the parallelization code away from the strategies themselves.
I don't think that this strategy framework it's useful for games where you are to make intelligent moves, such as Chess or Go. Moreover, even in "exploration" games (such as Battleship), it's ability to combine and decouple may just lose to a better tailored but a less decoupled counterpart. However, this framework is invaluable when you need too explore a complex and yet uncovered environment with limited resources, and want too experiment throughout the process.
Author Paul Shved
Modified June 9, 2012
License CC BY-SA 3.0