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Assignment 2: General Games
创建日期：2024-07-02 17:27:47
标签： algorithm
Assignment 2: General Games

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This assignment uses the General Video Game AI framework (GVGAI).  You can only use Java for this assignment.

Assignment 2 requires that you test existing controllers and develop and improve your own controller based on techniques covered during this course. We only consider the Single Player Planning Track in this assignment. For a quick start, take a look at src/tracks/singlePlayer/Test.java for an example on how to use the framework.

In this assignment, we only consider games from Training Set 1. For each exercise, you have to evaluate the considered controllers on the following 4 games: Aliens, Boulderdash, Butter ies, Chase. Note that each game includes 5 levels (from \lvl0″ to \lvl4″), so you will be looking at 20 game levels in total.

Exercise 1 Team work: who has done what? (zero points)

Just like in Assignment 1, we’d like each team member to write one paragraph about what he or she has contributed to this assignment. We will not mark this, and it will not have any e ect on the marking of the other exercises. You might now ask, \why do this then?” { well, through this no-stakes approach, we’d like to encourage self-regulation within the group and cooperative learning. You can’t lose; you can only win.

Exercise 2 Existing Controllers (30 points)

1. Run each of the three controllers 10 times on each of the 5 levels of each game (20 levels in total) and report for each game level the average score and standard deviation.

2. Change the \One Step Look-Ahead” Controller such that it looks more than 1 step ahead. Make sure that it does not exceed the time out and try to obtain a high performing controller.

3. Run your new \several steps ahead”-controller on each game 5 times and report the average score and standard deviation. In the report, you must brie y describe and justify the changes that you have made.

Exercise 3 Genetic Algorithm Controller (20 points)

Consider the \Genetic Algorithm” Controller

1. Design an evolutionary algorithm to optimise the parameter setting (GAMMA,SIMULATION DEPTH, POPULATION SIZE, RECPROB, MUT) for the \Ge-netic Algorithm” Controller for all levels of a considered game (5 levels). The objective is to maximise the scores this controller achieves on these games. In the report, you must brie y describe the algorithm and justify its design.

2. Give the best obtained parameter settings for each of the 4 games together with the results obtained for each game and compare it to the default parameter setting tested in Exercise 2. The comparison should be statistically meaningful (i.e. using statistical testing methods).

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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