Academic Dishonesty and Working with Classmates

This last project is an opportunity to be creative, so your ideas are your own intellectual property. You are welcome to incorporate other peoples' ideas and even code, as long as it is properly cited and licensed in your writeup. Feel free to share discoveries about the APIs and provided code with your classmates, ideally on the message board so everyone can benefit, but avoid giving away your strategy and any trade secrets, since you will be engaging in a friendly competition with your classmates too.

Layout

The Pacman map is now divided into two halves: blue (right) and red (left). Red agents (which all have even indices) must defend the red food while trying to eat the blue food. When on the red side, a red agent is a ghost. When crossing into enemy territory, the agent becomes a Pacman.

Scoring

As a Pacman eats food dots, those food dots are stored up inside of that Pacman and removed from the board. When a Pacman returns to his side of the board, he "deposits" the food dots he is carrying, earning one point per food pellet delivered. Red team scores are positive, while Blue team scores are negative.

If Pacman is eaten by a ghost before reaching his own side of the board, he will explode into a cloud of food dots that will be deposited back onto the board.

Eating Pacman

When a Pacman is eaten by an opposing ghost, the Pacman returns to its starting position (as a ghost). No points are awarded for eating an opponent.

Power Capsules

If Pacman eats a power capsule, agents on the opposing team become "scared" for the next 40 moves, or until they are eaten and respawn, whichever comes sooner. Agents that are "scared" are susceptible while in the form of ghosts (i.e. while on their own team's side) to being eaten by Pacman. Specifically, if Pacman collides with a "scared" ghost, Pacman is unaffected and the ghost respawns at its starting position (no longer in the "scared" state).

Observations

Agents can only observe an opponent's configuration (position and direction) if they or their teammate is within 5 squares (Manhattan distance). In addition, an agent always gets a noisy distance reading for each agent on the board, which can be used to approximately locate unobserved opponents.

Winning

A game ends when one team returns all but two of the opponents' dots. Games are also limited to 1200 agent moves (300 moves per each of the four agents). If this move limit is reached, whichever team has returned the most food wins. If the score is zero (i.e., tied) this is recorded as a tie game.

Computation Time

We will run your submissions on a machine at least as powerful as the machines in the CS department's rlogin cluster. Each agent has 1 second to return each action. Each move that does not return within one second will incur a warning. After three warnings, or any single move taking more than 3 seconds, the game is forfeit. There will be an initial start-up allowance of 15 seconds (use the registerInitialState function).

Baseline Team

To kickstart your agent design, we recommend looking at the baseline agents, defined in baselineTeam.py. They are quite bad. The OffensiveReflexAgent simply moves toward the closest food on the opposing side. The DefensiveReflexAgent wanders around on its own side and tries to chase down invaders it happens to see.

File Format

You should include your agents in a file of the same format as myTeam.py. Your agents must be completely contained in this one file.

Interface

The GameState in capture.py should look familiar, but contains new methods like getRedFood, which gets a grid of food on the red side (note that the grid is the size of the board, but is only true for cells on the red side with food). Also, note that you can list a team's indices with getRedTeamIndices, or test membership with isOnRedTeam.

Finally, you can access the list of noisy distance observations via getAgentDistances. These distances are within 6 of the truth, and the noise is chosen uniformly at random from the range [-6, 6] (e.g., if the true distance is 6, then each of {0, 1, ..., 12} is chosen with probability 1/13). You can get the likelihood of a noisy reading using getDistanceProb.

Distance Calculation

To facilitate agent development, we provide code in distanceCalculator.py to supply shortest path maze distances. This class smartly caches maze distances, making it very economical to use maze distances in your agents' reasoning.

CaptureAgent Methods

To get started designing your own agent, we recommend subclassing the CaptureAgent class. This provides access to several convenience methods. Some useful methods are:

Restrictions

You are free to design any agent you want. However, you will need to respect the provided APIs if you want to participate in the competition. Agents that compute during the opponent's turn will be disqualified. In particular, any form of multi-threading is disallowed, because we have found it very hard to ensure that no computation takes place on the opponent's turn.

Warning About Output

If one of your agents produces any stderr output during its games in an online match, that output may be included in the contest results posted on the website. Additionally, in some cases a stack trace may be shown among this output in the event that one of your agents throws an exception. You should design your code in such a way that this does not expose any information that you wish to keep confidential.

Layouts

By default, all games are run on the defaultcapture layout. To test your agent on other layouts, use the -l option. In particular, you can generate random layouts by specifying RANDOM[seed]. For example, -l RANDOM13 will use a map randomly generated with seed 13.