Extensive Auction Games
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Extensive Auction Games
Agent-based Coursework
1 Introduction
Imagine an auction of paintings by four famous artists: Picasso, Van Gogh, Rembrandt and Da Vinci. In the auction room the auctioneer presents each piece to be sold, and all bidders then write their bids for the item on a secret sealed note that is handed to the auctioneer. The auctioneer then declares the highest bidder the winner, takes a payment (equal to the bid of the second-highest bidder), and starts the next round with a new item. The auction continues until there are no more items to sell. The bidder with the paintings of the highest total value is the winner.
The objective is to implement strategies for a Python 3 bidding bot that will participate in this game.
Allwoed Python 3 Libaries: Allowed Python 3 Libraries On top of the standard library, we allow: matplotlib, numpy, pandas, scipy, statsmodels. For other libraries, please ask!
2 The Game
This is an extensive game with simultaneous moves encoded as an auction played sequentially. The game runs in the following way. An auctioneer initialises a room full of bots, then sets up the auction. This involves giving each bot the same budget to spend and setting the sequence of items (and their types) to be sold. This sequence is announced to the bidders.
The auctioneer will then sell the paintings one after another. In each round, the auctioneer will announce the item type (Picasso, Van Gogh, Rembrandt, or Da Vinci) to be bid upon and ask the bots for bids. Your bot will use the information that the auctioneer gives, including the outcomes1 of the previous rounds, to determine an amount to bid. Your bid cannot exceed your remaining budget. Once all bots have bid, the auctioneer will declare the highest bidder the winner, who will then be charged a payment (equal to the second highest bid, see below) and receives the item. If the top bids draw, then the winner is chosen at random from those bidders. For each round, there is exactly one winner.
The auction size (i.e., the number of paintings up for auction) will be 200 and the starting budget for every bidder is 1001. The auction will continue until all 200 items have been sold.
Payment rule: For each item, the highest bidder will win. However, the highest bidder does not pay their own bid. Instead, the highest bidder is only charged the second-highest bid. In case there are two or more bidders who tie for the highest bid, the winner is chosen uniformly at random from those bidders and pays the second-highest bid (which, in this case, is equal to their own bid).
Winning condition: Winning a painting is worth a certain number of points, depending on the type: Picasso is worth 2 points, Van Gogh is worth 12 points, Rembrandt worth is worth 3 points, and Da Vinci is worth 7 points. (Leftover budget is not worth any points.) The bidder who ends up with the highest total number of points is the winner of the game. If several bidders tie with the highest number of points, the winner is chosen uniformly at random from those bidders.
You will write your strategies in your bot. Your bot will be tested in a series of different auctions against bots of varying difficulty and number. Finally, your bots will be tested against each other in a tournament.
3 Implementation
3.1 auctioneer.py
auctioneer.py contains the definition for the auctioneer class, which sets up and runs the auction. We will use
this exact same file while marking your bots, so DO NOT CHANGE ANY OF THE CODE IN THIS FILE. It has the following arguments:
• room(list of modules): A “room” is a list of bots that will play the auction. The bots are module objects. There is an example of how to import them at the top of the auctioneer.py file, and how to pass them to the auctioneer as a list at the bottom of the auctioneer.py file. There are also examples in the arena.py file.
• painting order(list of strings): A list of the sequence of painting types that will be auctioned in each of the rounds. If this is set to None then a random order will be used.
• slowdown(float): How long to wait at each round of the auction. If you set this to zero the auctions will run fast.
• verbose(boolean): Whether the auctioneer prints updates to the terminal or not.
• output csv file(string): The auctioneer automatically logs the result of every round in an auction. This defaults to ’data/auctioneer log.csv’, but you can specify a different filename.
When the auctioneer is initialised it will automatically set up the auction and all the bots in the room with the initialise bots() method.
The method run auction() will run the auction until it is completed.
It is not necessary to know how the auctioneer class works to do well on the coursework. But if you are interested there are more details in the README.md file.
3.2 arena.py
The arena.py file is provided as a convenient way to run auctions. This includes some methods that show examples of how to run auctions. This is given to you as an example, so please feel free to change any of the code here and experiment.
You might want to run an auction slowly, with a full print out to the terminal of the auction’s progress, as shown in run basic auction(). This will be useful to see how your bot is performing live. Or you might want to run lots of auctions quickly, as shown in run lots of auctions().
3.3 bots
In the bots folder we included a few bots for you to practice with. We have given you 3 bots. bots/u1234321.py is a good starting point for your own bot, with everything commented clearly. bots/flat bot 10.py and bots/random bot.py are example bots that should be easy to beat.
3.4 bots/u1234321.py
u1234321.py is an example bot that you can use as a starting point to write your own bot.
Each bot is a class, with one main method, get bid(), that allows you to make bids on paintings. It is in this method that you should implement your strategy. The comments on the method explain what each argument is. Currently, the strategy just returns a random integer between 0 and the amount of budget that your bot has left.
Your bot will be initialised at the start of the auction, and then will play until the auction finishes. This means that your bot can hold internal state variables during the auction, which may be useful for some strategies.
You can add these variables, or any other code, in the init () method, if you want to. Please change the self.name=“1234321” variable to your own ID number.
Feel free to add any extra methods that you might need, but be careful that the main method keeps the same input variables and returns an integer bid without crashing.
If your bot crashes then your bid will be set to zero. If your bot returns a float then this will be rounded down to an integer. If your bot bids more than their budget then their bid will be set to zero. If your bot takes longer than 5 seconds to return a bid, then again your bid will be set to zero.
3.5 bots/random bot.py and bots/flat bot 10.py
We’ve included some simple bots for you to play against.
• random bot.py: Bids a random integer between 0 and the bot’s remaining budget.
• flat bot 10.py: Bids 10 on everything.
You can add your own. You can see how to import bots and play against them in the arena.py file.
4 Submission
Your coursework submission will consist of a single compressed file (either .zip or .tgz) containing your bot in a python file and your writeup as a pdf. The coursework file should be submitted through Tabula.
4.1 Your Bot
Please save this as u
u1867321.py
class Bot(object):
def __init__(self):
self.name = 1867321
....
def get_bid(self, ....):
....
We recommend that you can use bots/u1234321.py as a starting point.
Here is an example of how your bot will be imported and run:
from auctioneer import Auctioneer
from bots import random_bot
from bots import u1867321 # Your id number here
room = [random_bot, u1867321]
game = Auctioneer(room=room, slowdown=0)
winner = game.run_auction()
print("The winner is ", winner)
Please make sure that your bot will run in this way, replacing the id number here with your own id number. If you run the above script you should see the auction run very fast and then print out “The winner is [1867321]”, with your id number shown (if your strategy can beat a random bot).
You can test that your bot runs correctly by using the functions provided in the arena.py file. If your bot crashes you will lose marks!
4.2 Your Writeup
Please save this as
5 Evaluation
The coursework is worth 25% of the module credit. Its marking scheme is structured as follows:
• 
of the total mark: Strategy performance against various bots including other submissions.
• 
of the total mark: Quality of the written report (how well you explain and analyse your strategies).
Your bot will be tested in a range of different auction “rooms”, made up of bots of different difficulties, in different room sizes and compositions. For example, you will be tested in a room against one random bot. You will also be tested in a room with a mix of up to 10 bots with different strategies. Your bot’s performance in this set of rooms will form the majority of your mark for strategy performance.
We will also play the student bots against each other, and this will contribute to your mark for strategy performance.
6 Cheating/Plagiarism
All submissions will be put through plagiarism detection software which compares against a number of sources, including other submissions for submissions at other universities, web sources, conference papers, journals and books.
In addition, please note that any attempt to access other bots, modify the auctioneer class or otherwise intervene in the fair running of the auctions will be considered cheating and sanctioned accordingly.
2023-03-30