report代做 | GUI代写 | Algorithm作业 | 作业shell | Python | AI – CONNECT 4


report代做 | GUI代写 | Algorithm作业 | 作业shell | Python | AI – 这道题目是利用report进行的编程代写任务, 涵盖了report/GUI/Algorithm/shell/Python/AI等方面

AI代写 代做机器学习 ai代做 machine learning代写 ML代做


Are you ready for the 2022 Computer Connect-

Regional Championship Tournament? Will you be

the one to write the Algorithm to win the title?

Enter the competition today!

See the full list of rules and regulations below

Connect- 4 Regional Championship Handbook Rules and Regulations The Connect 4 Regional Championship (C4RC) is a team-based computer connect-4 tournament where participants will design an algorithm to efficiently and competitively play the game of connect-4. The following regulations are in place: Competitors are free to use any algorithm and evaluation function they like, so long as the computation is done on the local machine (e.g. no online API calls) The exception to the above rule is that exactly one move may be hardcoded. We recommend this to be the first move. The allowed time per move is limited to 0.5 seconds CPU time – that is, the time the CPU dedicates to running that process. This will be enforced by the driver code and you do not need to time your own algorithm. You should, however, ensure that you set the depth you will explore in accordance with this rule. The code for this competition will be written in Python 3.x. Participants are generally free to import any publicly available libraries so long as they do not make the task trivial. If you have any questions about this rule, please reach out to C4RC staff. Teams are to submit a report detailing their method and results against provided benchmark agents. This report will be presented to the design committee. Best of luck to all of our contestants!

Connect- 4 Regional Championship Handbook Report Guidelines Along with the code, contestants will submit a report to the design committee to ensure fairness and integrity in the competition. The report will consist of five parts, some of which will be written and others coded. This report will be submitted as a PDF of any format (eg slides, document), and the code submitted should be the file with alphaBeta AI implemented. Teams will present their report to the design committee. Part I: Evaluation Function Please explicitly state your evaluation function for terminal nodes. Your report must cover the following: An explicit mathematical function to find the evaluation function from a given board A brief motivation for the evaluation function (a couple of sentences to one paragraph) A worked example of a midgame board showing the evaluation function score Part II: Coding the Agent Using minimax with alpha-beta pruning, code your algorithm to play the game. This must inherit from the Connect4Player class. Part III: Evaluation Function Evaluate your agent against the benchmark agents. vs StupidAI vs RandomAI 5 times

Connect- 4 Regional Championship Handbook vs MonteCarloAI 10 times As player 1 and as player 2. Report your results in a table. For RandomAI and MonteCarloAI, set the seeds 1-5 for reproducibility. Note that while RandomAI and StupidAI can be seen as sanity checks that your algorithm is working, MCAI is a much more competitive player, but certainly beatable. Winners of previous contests typically beat MCAI all 20 times. Interview You will present your report to the design committee on the date & time of your choosing, in accordance with their availability. You will have 10 minutes in total, 8 of which will be reserved for your presentation and 2 for questions and answers. Your team will be expected to speak and answer questions in an egalitarian manner, hopefully splitting up the time evenly, with no single individual taking the majority of the time alotted. In your interview, cover the following: Summarize your answer to Part I. Use your written answer as a guide, but do not read directly from it. Describe how testing and finetuning influenced the final form of your evaluation function. Explain briefly (less than 1 minute) why your algorithm makes sense for this task Explain briefly (less than 1 minute) the steps you took to increase the efficiency of this task (eg ordering of nodes) Once as player 1 and once as player 2, demonstrate your minimax implementations performance against: StupidAI RandomAI

Connect- 4 Regional Championship Handbook Demonstrate your implementations performance against MonteCarloAI four times, twice as player 1 and twice as player 2. Where appropriate, you may discuss one topic while demonstrating another. For instance, you may discuss how testing influenced your evaluation function while your agent is playing against the benchmarks.

Connect- 4 Regional Championship Handbook Important Functions & Calls Commandline Interface Command Description Datatype Example Default -p1 Agent who will be acting as player 1. Name of agent eg minimaxAI Stirng -p1 minimaxAI -p1 monteCarloAI human -p2 Agent who will be acting as player 2. Name of agent eg minimaxAI String -p1 minimaxAI -p1 monteCarloAI human -seed Seed for AIs with stochastic elements int -seed 0 0 -w Rows of gamebaord int -w 6 6 -l Columns of gameboard int -l 7 7 -visualize Bool to use or not use GUI bool -visualize True -visualize False True -verbose Sends move-by-move game history to shell bool -verbose True -verbose False False -limit_players Which agents should have time limits. Useful if you want to play an AI but dont want to have the same time Stirng -limit_players 1, -limit_players -1, (player 1 is not limited) -limit_players 1,-1(player 2 is not limited) 1,

Connect- 4 Regional Championship Handbook limit. In the format x,y where x and y are players. Values that are not 1 or 2 can be used in place of 1 or 2 if the player should not be limited -time_limit Time limit for each player. No effect if a player is not limited. In the format x,y where x and y are floating point numbers. Stirng -time_limit 0.5,0.5 0.5,0.