PacMan capture-the-flag: a fun game for artificial intelligence development and education

At the beginning of September I’ve been invited to teach at a summer school about scientific programming. The whole experience has been really rewarding, but it was the student’s project that got me going: we had the students write artificial intelligence algorithms for the agents of a PacMan-like game, and organized a tournament for them to compete against each other.

The PacMan capture-the-flag game has been written originally by John DeNero, and has been used to teach an artificial intelligence course by him at Berkley and by Hal Daume III at University of Utah. Very often, this kind of games have a single strategy that dominates all others, and once you find it the interest fizzles out. In this case, I was impressed by how rich this game is. The game offers a lot of opportunities to develop and test complex learning and planning algorithms, including cooperation strategies for games with multiple agents.

capture_the_flag

The rules of the game are quite simple: the board is a PacMan maze, divided in a red and a blue half. The two halves belong to two teams of agents, who are controlled by computer programs to eat the opponent’s food and protect their own. When in the opponent’s half, the agents are PacMan (PacMen?), while in their own half, the agents are ghosts and can kill the opponent’s PacMan agents, in which case these are returned to their initial position. The players get one point for each food dot they eat; no points are assigned for eating the other team’s agents. The game ends when one of the two teams eats all of the opponent’s food, or after 3000 moves; the team with the highest score wins.

To make the game more interesting, one can only observe the position of the other team’s agents when they are very close to one’w own agents (5 squares away); otherwise, one can only obtain a noisy estimate of their distance.

The game is written in Python, my programming language of choice, which allows to write rapidly even sophisticated algorithms. I recommend the game to anyone wanting to organize an artificial intelligence course, or simply have fun writing AI agents. I plan to dedicate a couple of posts to the basic strategies to write successful agents in this game.

Here’s a video of the best students’ agents (red team) playing against the best tutors’ agents (blue team). The tutors won, saving our reputation!

Update: The authors of the PacMan capture-the-flag game decided to keep the game close-source, and in particular would prefer not to publish the code of agents playing their game, fearing that it might interfere with their course. It’s a shame because I was planning to write some Genetic Programming agents for the game, but of course I respect their decision. I guess there will be no series of posts re:PacMan…

My AI reads your mind — Extensions (part 3)

In the previous two posts I showed how to make use of decision theory to write a game AI that forms a model of its opponent and adapts its strategy accordingly.

The AI could be improved in several ways:

  • The most obvious improvement would be to build a better model of the opponent. In the Karate game I used a 2nd order Markov model, i.e., I assumed that the next move of the player only depends on his previous two moves. It is of course possible to use an higher-order model, that would keep track of three or more past moves. However, a long history means a much larger number of parameters to estimate, so that it will take much longer for the AI to have a reasonable estimate of the player’s behavior. An easy workaround would be to collect higher-order statistics, but only use them when enough data is available; this however would still fail if the player decides to adopt a new strategy. One could also use another class of models, like, for example a recurrent neural network. I prefer not to use neural networks, first of all to avoid the usual voodoo of choosing parameters like learning rate, network architecture, etc., and second because they work in a black-box fashion, which makes it difficult to extend them in principled ways, as for example suggested below.
  • When the game is started, the player statistics are blank, in the sense that all the player’s moves are considered equally likely. This is our initial prior probability for the opponent’s moves. However, it is probable that humans have common biases in the kind of action sequences they choose. One could adapt the game to allow it to learn this biases over many games with different players, and use them as the initial prior, thereby improving the initial phase of the game.
  • A related point is that at the moment I do not take into account the uncertainty about the player’s move estimation. At the start of the game the AI has only a few examples on which to base its prediction of the next move, and it should take this into account when making decisions. The formal way to capture this uncertainty is to define an hyperprior on the transition probabilities, and then integrate over it when predicting the next move: so far, the transition probability is given by a matrix, p(n(t+1) | n(t)) = N_{n(t),n(t+1)}, where N is a matrix and for simplicity I’m only considering a first order Markov model. N is considered to be a constant at every time point; in reality, N is also a random variable that is being estimated from the player’s move. It is thus only natural to put a prior on N itself, P(N) (e.g., a Dirichlet distribution for every row of the matrix). The improved prediction, which takes into account our uncertainty about N is given by p(n(t+1) | n(t)) = integral over N of p(n(t+1) | n(t), N) p(N) .
  • In the karate game, the AI tries to maximize its score by picking the action with the highest expected score. Another strategy would be to choose every action with a probability related to the score, for example using p(choosing action a) to be proportional to exp(beta * score of a), where beta is a constant that controls the “softness” of the decision: for beta=0, all actions are chosen with the same probability; for large betas, only the action with largest score is chosen. This seems to be closer to the way human takes decision, the so called probability matching rule. This strategy is suboptimal if the second order Markov model is the real model of the opponent, but since it is not, it appears to perform better in practice as the AI becomes less predictable (I updated the Karate game in the previous post to do probability matching).
  • A major improvement to the AI would be to take into account the so-called theory of mind, i.e., the fact that while the AI is building a model of the player, the player is doing the same for the AI, and trying to maximize his own score. Taking this aspect into account is quite complex, as one falls rapidly into a deep I-know-that-you-know-that-I-know kind of reasoning. Managing to do so, however, is likely to be highly rewarding for the player’s experience of the game: several studies have shown how humans activate areas of the brain that are associated with theory-of-mind when playing against a human opponent, but fail to do so against a computer (see Gallagher and Frith, Functional imaging of ‘theory of mind’, Trends in Cognitive Sciences, 7(2), 2003, for a review). It is thus possible that by writing computer programs that make use of theory of mind themselves, those centers would become engaged, giving the player the impression of playing against a human opponent.

My AI reads your mind and kicks your ass (part 2)

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In the last post I discussed how it is possible to program a game Artificial Intelligence to exploit a player’s unconscious biases using a simple mathematical model. In the karate game above, the AI uses that model in order to do the largest amount of damage. Give it a try! You get 10 points if you hit your opponent with a punch or a kick, 0 points if you miss, and 5 points if you block your opponent’s move. As you play, the AI learns your strategy and adapts to knock you down as often as possible.

How does it work? According to decision theory, we need to maximize the expected score. To compute the expected score for an action ‘x’ (e.g., ‘punch’), one needs to consider all possible player’s moves, ‘y’, and weight the possible outcome with the probability of the player doing that move, i.e.

E[score for x] = sum_y P(y) * Score(y,x)

where P(y) is the probability of the player choosing action ‘y’ (obtained using last post’s model), and Score(y,x) gives the score of responding ‘x’ to ‘y’.

For example, in the karate game using a low kick has a priori the highest chance of success: you score in 3 out of 4 cases, and only lose 5 points if the opponent decides to block your kick. This is why, at the beginning, the AI tends to choose that move. However, if you know that the AI uses that move often, you will choose the kick-blocking move more often, increasing P(kick-block). This change will make the punch more likely to score points. As you play, the optimal strategy changes and the AI continues to adapt to your style.

With a bit of practice, you’ll notice that you can compete with the AI and sometimes even gain the upper hand over it. This shows that you are in turn forming an internal model of the computer’s strategy. I think that the game dynamics that results from this interaction makes the game quite interesting, even though it is extremely simple. Unfortunately, it’s very rare to see learning AIs in real-life video games…

As always, you can download the code here.

Update: Instead of always making the best move, the AI now selects the move with a probability related to its score, which makes it less predictable. More details in the next post…

My AI reads your mind (part 1)

I regularly read about people complaining that AI in games should be improved. I definitely agree with them, but here’s a argument why pushing it to the limits might not be such a good idea: computers can easily discover and exploit our unconscious biases.

Magic? ESP? More like a simple application of decision theory. In order to make an unbeatable AI one needs two steps: 1) build a model of a player’s response in order to predict his next move, and 2) choose actions that maximize the expected score given the prediction of the model.

The basic idea behind 1) is that even if we try to be unpredictable, our actions contain hidden patterns that can be revealed using a pinch of statistics. Formally, the model takes the form of a probability distribution: P(next move | past observations).

Try it out: In the Flash example below, you can type in a sequence numbers 1-4, and the AI will try to predict your next choice. If your choices were completely random, the AI would only be able to guess correctly 25% of the time. In practice, it often guesses correctly 35-40% of the numbers! (It might take a few numbers before the AI starts doing a decent job.)

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In this example I used a 2nd order Markov model, i.e., I assumed that the next number, n(t+1), only depends on the past 2 choices: P(n(t+1) | past observations) = P(n(t+1) | n(t), n(t-1)). The rest is just book-keeping: I used two arrays, one to remember the past 3 numbers, and one to keep track of how many times the player chose number ‘k’, given that his past two moves were ‘i’ and ‘j’:

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// last 3 moves
public var history:Array = [1, 3, 2];
// transition table: transitions[i,j,k] stores the number
// of time the player pressed 'i' followed by 'j' followed by 'k'
public var transitions:Array;

When the player makes a new choice, I update the history, and increment the corresponding entry in the transition table:

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/*
* Update history and transition tables with player's move.
*/

public function update(move:int):void {
history = [history[1], history[2], move];
transitions[history[0]][history[1]][history[2]] += 1;
}

The probability that the next choice will be n(t+1), is given by the number of times the player pressed n(t+1) after n(t) and n(t-1) before, normalized by the number of time the sequence n(t-1), n(t) occurred in the past:

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/*
* Return probability distribution for next move.
*/

public function predict():Array {
// probability distribution over next move
var prob:Array = new Array(4);

// look up previous transitions from moves at time t-2, t-1
var tr:Array = transitions[history[1]][history[2]];

// normalizing constant
var sum:Number = 0;
for (var k:int = 0; k < 4; k++) {
sum += tr[k];
}

for (k = 0; k < 4; k++) {
prob[k] = tr[k] / sum;
}

return prob;
}

The best prediction is given by the choice with maximum probability. You’re welcome to have a look at the code!

In the next post, I’ll show how the AI can choose the best actions in order to maximize its expected score in a Virtual Karate game.

Advanced math functions for AS3

Did you ever need to sample from a Dirichlet distribution in Actionscript 3.0? What about computing a value of the Gamma function? Probably not ;-) In case you did, I wrote a class with a few math methods:

  • a function to sample from multinomial distributions; this is useful if you need to choose randomly from a set of elements, each with a different probability;
  • functions to draw samples from the Dirichlet and Gamma distribution;
  • the Gamma function
  • a factorial function that works even for large values (using the identity n! = Gamma(n+1))
  • other utility functions, e.g. to compute mean and variance of an array, or add two arrays together

You can download the AdvancedMath class here, it includes AsUnit tests.

This is an example of how to use the AdvancedMath class to simulate a rigged dice: the code below throws the dice 1000 times, and keeps a count of how many times one gets the different faces.

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import com.masterbaboon.AdvancedMath;

// probability of the faces; 6 is about twice more likely to appear
var p:Array = [0.14, 0.14, 0.14, 0.14, 0.14, 0.3];
           
// count the number of times a face appears
var count:Array = AdvancedMath.zeros(6);
           
// throw dice 1000 times
var face:int;
for (var i:int = 0; i < 1000; i++) {
    face = AdvancedMath.sampleMultinomial(p);
    count[face]++;
}
           
// show how many times we got which face
trace(count);

Guess 2/3 game: sending data from Flash to a remote database

I wrote a simple game to learn how to store on and retrieve data from a server-side database. The rules are simple: you have to guess 2/3 of the average of the guesses of all the players. So, for example, if you think that the other players guessed on average 100, your guess should be 67. The 2/3 rule is there to avoid the trivial strategy of  submitting the same number over and over again. According to game theory, the game has a Nash equilibrium at 0, which in this case does not correspond to a rational strategy. Wikipedia has some information and links about the game. Give it a try:

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The score you see after submitting is the distance from your guess to the true 2/3 of the average. Since you get some information about your and others’ score, it should be possible to find a strategy to win in a small number of trials… Let me know if you find one!

Communicating with the server

To implement the game, I created a MySQL table in a database on the server that is used to store the players’ name and guess. The database is accessed by calling a server-side PHP script from the game Actionscript client.

The PHP script gets the player’s information and stores it in the database; then it computes the 2/3 of the average of all entries, and returns the score and rank of the current player, along with the top scores in the database. The script receives the data as a HTTP request (the ?var1=value1&var2=value2 thing you see in many dynamical web pages), and returns an XML fragment.

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\n";
// top scores
while (($row = mysql_fetch_array($hit)) &amp;&amp; ($currentrank==0 || $rank&lt;=$ntop)) {
  if ($rank&lt;=$ntop) {
    print "
\n";
    print "
\t{$row['name']}\n";
    print "
\t{$row['diff']}\n";
    print "
\n";
  }
  if ($row['time'] == $time) {
    $currentrank = $rank;
    $currentscore = $row['diff'];
  }
  $rank = $rank + 1;
}

// current score
print "
\n\t{$name}\n\t{$currentscore}\n";
print "


\n";

mysql_close();

?&gt;

Calling the script from AS3 is relativelty easy. The remote call is done by a URLLoader object; the actual request is stored in a URLRequest object, and the request variables are wrapped in a URLVariables instance. I had an annoying problem with testing the code: for some reason, Flash always caches the requests. I looked around and it seems that the only workaround is to add a dummy variable to the request with a value that changes constantly, e.g., the current time.

This is the code for the communication with the server:

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public function submitGuess(name:String, guess:uint, ntop:int):void {
    var request:URLRequest = new URLRequest("http://www.masterbaboon.com/php/guess23/guess23submit.php");
    var httpHeader : URLRequestHeader = new URLRequestHeader("pragma", "no-cache");
    request.requestHeaders.push(httpHeader);

    // request variables: name of player, guess, number of top scores to return
    var vars:URLVariables = new URLVariables();
    vars.name = name;
    vars.guess = guess;
    vars.ntop = ntop;
    // this is to trick AS3 into not caching the URL request
    vars.nocache = Number(new Date().getTime());

    request.method = URLRequestMethod.GET;
    request.data = vars;

    // send to server and call loadingComplete when complete
    var loader:URLLoader = new URLLoader();

    // the results of the request is not returned immediately, but
    // we can monitor the COMPLETE event dispatched by the loader
    loader.addEventListener(Event.COMPLETE, loadingComplete);
    try {
        loader.load(request);
    } catch (e:Error) {
        trace("An error has occurred while communicating with the server.");
        trace(e);
    }
}

public function loadingComplete(e:Event):void {
    // this is how to access the data returned from the request
    data = XML(e.target.data);
    // switch frame
    gotoAndStop("highScoreFrame");
}

You’re welcome to download the whole code.

Simulated evolution

Here’s another classic from the 80’s: an artificial life simulation, where bugs move on a virtual Petri dish, hunting for bacteria. If they manage to survive until adulthood, and accumulate enough energy from bacteria, the bugs reproduce and generate two copies of themselves. In the reproduction process, the genetic code undergoes small mutations, so that the baby-bugs are not exact copies of their mother.

Get Adobe Flash player

The genetic code of bugs determines the way they move around. It consists of six numbers that give the probability that the bug will move  in one of six directions (forward, soft/hard right, backwards, soft/hard left) at any point in time. For example, a bug with code [5, 0, 5, 0, 0, 0] would move forward 50% of the time (relative to his current direction), and for the rest of the time it would take a hard turn right (120 degrees on its right) and move. Mutations change one of the numbers in the code by +/- 2.

Individual with a genetic code unfit to deal with competition for food eventually die away, and by the law of natural selection the population of bugs adapts to efficiently navigate their environment to collect bacteria. The optimal strategy will depend on the environment: if the bacteria are randomly scattered around, the optimal behavior is to “slide” forward for some time before taking a turn. If instead bacteria are concentrated on a small patch, a surer way for a bug to survive is to rotate on itself, to make sure not to get too far.

This idea was described by A.K. Dewdney in in the article “Simulated evolution: wherein bugs learn to hunt bacteria” in 1989  in Scientific American (May, pp. 138-141). The flash application above is my version of Dewdney’s simulation, implemented in ActionScript 3 (click here to download the code). It is based on the SpatialDatabase class described in the previous post, so you might want to have a look at the code if you’re curious about how it can be used in practice.

As you might have guessed, the yellow circles are bacteria, while the green ones are bugs. Bugs start to fade when their energy is low; if they don’t find food fast enough, they eventually disappear into nothing. The button “Garden of Eden” activates a small region with high bacterial growth, you can switch it on to see how fast the bugs adapt to the new environment. It usually takes around 20 generations for them to show a highly specialized behavior.

Have fun!

Spatial database for collision detection

In games and other graphical applications one has to keep track multiple sprites and detect collisions between them. A naif approach would loop over all sprites and check for collision with *every other sprite*. This is, of course, terribly inefficient and can be very slow even for a small number of sprites.

One way out of this nightmare  is to register the sprites in a spatial database that stores them according to their position, and is able to determine which of them is close to a given point. As a result, one only needs to check for collision between neighboring sprites. There are many implementations of spatial databases, some of which are quite sophisticated. In this post I’m going to describe an ActionScript 3 implementation of a grid-based spatial database, that can be used for simple flash games.

Grid: 2D Array with neighborhood

A Grid is a two-dimensional array with a concept of neighborhood. In addition to be able to store and retrieve information on the 2D grid, one can request neighboring elements of a given array element.

For example, let’s create a simple 4×4 grid, and store at each point an increasing number:

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var grid:Grid = new Grid(4, 4);
var k:int = 1;
for (var i:int = 0; i &lt; 4; i++) {
    for (var j:int = 0; j &lt; 4; j++) {
        grid.set(i, j, k++);
    }
}

This image shows the resulting grid, with small numbers indicating grid coordinates and large, bold ones the stored values:

grid11

We can now query the grid to get neighbors of a given position: For example, the following code

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var neighbors:Array = grid.getNeighbors(1, 1);
trace(neighbors);
neighbors = grid.getNeighbors(1, 3);
trace(neighbors);

displays 1, 2, 3, 5, 7, 9, 10, 11 and 3, 4, 7, 11, 12, respectively, as shown here:

grids2and3

Note that, by default, the neighbors stop at the border. It is possible to work on a “toroidal” grid, meaning that the opposite borders of the grid are connected:

grid4

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grid.setToroidal(true);
neighbors = grid.getNeighbors(1, 3);
trace(neighbors);

which prints 3,4,1,7,5,11,12,9 .

I added two functions that simplify working with neighbors. The first, mapOnNeighbors(x, y, fct), applies a function fct to all neighbors of (x,y). Let’s  see which of the neighbors of (1,3), are even numbers, just for fun:

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function isEven(x:int):Boolean { return (x % 2 == 0); }
var neighborsAreEven:Array = grid.mapOnNeighbors(1, 3, isEven);
trace(neighborsAreEven);

This gives false,true,false,false,false,false,true,false. There are several interesting uses of this method: collecting information from elements in a region of the grid, activating neighboring elements, …

The second function, reduceOnNeighbors(x, y, fct), is a bit more difficult to explain, but equally useful: it returns a single value constructed by iterating over the neighbors of (x,y) and calling fct(a, b) on the first two items of the sequence, then on the result and the next item, and so on. We can use this function to compute the sum of all neighbors:

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function sum(x:Number, y:Number):Number { return x + y; }
var sumOfNeighbors:Number = grid.reduceOnNeighbors(1, 1, sum);
trace(sumOfNeighbors);

prints 48 = 1+2+3+5+7+9+10+11 . This function could have been used for example in the previous post in the Cellular Automaton code, to compute the number of alive neighbors for every cell of the CA.

SpatialDatabase

The SpatialDatabase class registers sprites in a Grid with coarser resolution. For example, a Shape at position (31, 23) on the stage would be stored in element (3,2) if the resolution of the grid is 10 pixels.

That’s basically all there is to it! For collision detection, we can query the spatial database for sprites registered at neighboring elements in the grid:

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// Check collision with shape:Shape
// 1. get neighbors
var neighbors:Array = spatialDatabase.getAllNeighbors(shape.x, shape.y);
// 2. loop over all neighbors and check collision
for each (var s:Shape in neighbors) {
    // check collision
    if (checkCollision(shape, s)) {
        // do something (bounce, explode, ...)
    }
}

A higher resolution gives an optimal performance. Just keep in mind that the grid size should be larger than the size of the largest sprite, otherwise some collisions may go undetected.

There exist much more sophisticated methods to improve the efficiency of collision detection (see for example this post and the excellent tutorial at metanet software). However, this simple grid-based approach can be *very* efficient for many applications!

You can grab the code for the Grid and SpatialData`base classes here (including AsUnit tests for the Grid class). I have a nice example of this class at work, but this post is already long enough, have a look at the next one!

2D Cellular Automata

I decided to get my feet wet with Flash + ActionScript programming with a classic of the 80’s: 2-dimensional Cellular Automata!

A 2D CA is a grid of cells, each of which can be in either an “alive” or “dead” state. The state of each cell evolves in time, according to simple update rules based on the number of alive neighbors (each cell has 9 neighbors). Briefly:

  • The update rule defines the overall behavior of the CA, and is given by two lists of numbers, S for “Survival” and B for “Birth”
  • If the cell is alive and the number of active neighbors is not on the S list, the cell dies
  • If the cell is dead and the number of active neighbors is on the B list, the cell becomes alive

The standard notation for the rules is S/B. For example, 23/3 (S=[2,3], B=[3]) corresponds to the celebrated Game of Life by Conway, that produces ever-changing patterns of activity. The reason CAs are so famous is because they are a perfect example of how simple, local rules can produce complex, global behavior.

In the Flash application below, you can experiment with different rules by (un)checking the checkbox on the right side. This wikipedia page has a list of rules known to produce interesting behavior.

Get Adobe Flash player

Short instructions: click on the grid elements to switch them between dead and alive states. On the right side, you can add numbers to the Survival and Birth list, clear the CA to a blank state, or set the cells to a random state.

Actionscript notes

You can download the AS3 and the Flash library here. This is my first ActionScript project, so everything was new to me. The hardest part for me was to figure out how to link the interface I designed in the Flash IDE with the AS3 classes. I think I found a decent solution in the end, but let me know if you have suggestion to improve the code.

The BinaryCA class is an independent class to manage 2D CAs. To store the CA cells I based the core on the Array2 class from polygonal labs’ AS3DS data structures library.

Cellular Automata…  so retro!