本次澳洲**Java游戏代写**是游戏输入序列计算的一个assignment

## 1 Overview

Assignments should be done in groups consisting of five to six (5{6) students. Please use

the Groups feature in myUni. If you have problems finding a group use the Discussions

forum to search for group partners.

Each student has to take major responsibility for one of the exercises and collaborate

with the team members on the remaining exercises. Each exercise needs 1{2 students

taking major responsibility. For Exercise 4, you should have 2{3 students taking major

responsibility. The group has to make sure that the workload is evenly distributed.

## 2 Assignment

This assignment uses the General Video Game AI framework (GVGAI). Detailed infor-

mation about the competition and GVGAI is given at http://www.gvgai.net/. You

can get the code at https://github.com/GAIGResearch/GVGAI. You can only use

Java for this assignment.

Assignment 3 requires that you compute and test sequences of inputs to be played for each

game level. 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, you have to consider the following 4 games: Bomber, Boulder-

chase, Chase, Garbagecollector. These games are deterministic, meaning performing

a sequence of inputs results in the same outcome every time. Note that each game

includes 5 levels (from \lvl0″ to \lvl4″), so you will be looking at 20 game levels in total.

Since a sequence of inputs can be simulated using the advance function (see \One Step

Look-Ahead” controller) without having a game instance running, there will be no wall-

clock time constraint. Such a sequence is to be applied to the initial game state, and its

score is measured after applying the last input, or when a terminal game state (win or

lose) is reached during the simulation, whichever comes first. The score should be taken

directly from the game state without modification from heuristics.

For this assignment, we consider a call to the advance function to be the time unit for

the benchmark, as it is the bottleneck operation in the optimisation. Note that if the

simulation of a given sequence reaches a terminal game state, subsequent steps will not

change the game state, or incur computational cost. This means different sequences can

have different evaluation costs, which must be accounted for in the termination criteria

of your algorithms.

### 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 effect 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 Single-objective inputs optimisation (20 points)

For this exercise, you have to evolve a sequence of input that maximises the score on a

given game level.

1. Design an evolutionary algorithm to compute an input sequence maximising the

score on a game level. Describe and justify its design in the report. It should make

use of (and count the number of calls to) the advance function. You may use

existing techniques if appropriate.

2. Run the algorithm on each of the 20 game levels 10 times (or more) with 5 000 000

calls to advance each time, and report for each game level the average and standard

deviations of scores after 200 000, 1 000 000, 5 000 000 calls.

**程序代写代做C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB**

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