I am a legend: Hacking Hearthstone with machine-learning Defcon talk wrap-up
I am a legend: Hacking Hearthstone with machine-learning Defcon talk wrap-up: video and slides available but no tool.
Good news! The video and slides of our talk on how to use machine learning for Hearthstone are finally available for those who couldn't come to Defcon.
In this talk, Celine and I demonstrate how to use data analysis to find undervalued cards and how to exploit the game’s structure using machine learning to predict the opponent's deck.
You can see the slides on Slideshare and the video on YouTube:
Why are you not releasing your tool?
One thing you won't see posted, however, is the software tool that we promised to release during our Defcon presentation. Following Defcon, we had a series of conversations with the Hearthstone team about our research. Apparently the email that I sent prior to Defcon didn't reach the right person.
Here is a short summary of what they told us:
They like our research on the game/cards balance and are very enthusiastic and supportive about it.
On the other hand, they were very concerned that our real-time dashboard, which can predict an opponent's deck, will break the game balance by giving whoever has the tool an unfair advantage.
They also expressed concern that such a tool makes the game less fun by taking away some decision-making from the player.
It was a difficult decision - I have invested a lot of our time building our real-time dashboard tool with Celine - but we agree with the Hearthstone team and will not release the tool publicly.
因为暴雪不同意,所以没有发布这个工具。
How about game replays?
Beside predicting an opponent’s deck, the tool was geared to provide replay functionality to improve your game play and it allows us to collect data for our card balance analysis.
However, the game team told us that adding replay functionality to Hearthstone was in the road map.
Additionally as of October 2016, HSReplay offers a better way to collect replays, which is why we won't release a tool to do this either.
How can I learn more about this research?
A more “scientific” treatment of some of the talk results are published in this research paper.
If you want to learn more about applying machine learning to Hearthstone, you can read the following blog posts:
- How to price Hearthstone cards: Presents the card pricing model used in the follow-up posts to find undervalued cards.
- How to find undervalued cards automatically: Builds on the pricing model to find undervalued cards automatically.
- Pricing special cards: Showcases how to appraise the cost of cards that have complex effects, like VanCleef.
- Predicting your Hearthstone’s opponent deck: Demonstrates how to use machine learning to predict what the opponent will play.
- Predicting Hearthstone game outcomes with machine learning: Discusses how to apply machine learning to predict game outcomes.
好像漏掉了一篇文章,详情还是看https://elie.net/tag/hearthstone/
How to appraise Hearthstone card values
How to find undervalued Hearthstone cards automatically
I am a legend: hacking hearthstone with machine learning
Pricing hearthstone cards with unique abilities: VanCleef and The Twilight Drake
Edwin VanCleef
艾德温·范克里夫
"LocStringZhCn": "<b>连击:</b>在本回合中,你每使用一张其他牌,便获得+2/+2。",
Twilight Drake
暮光幼龙
"LocStringZhCn": "<b>战吼:</b>\n你每有一张手牌,便获得+1生命值。",
Predicting a Hearthstone opponent’s deck using machine learning
I am a legend: Hacking Hearthstone with machine-learning Defcon talk wrap-up
Hearthstone 3d card viewer in pure javascript/css3
Predicting Hearthstone game outcome with machine learning 预测对战结局
I am a legend hacking hearthstone using statistical learning methods
I am a legend: Hacking Hearthstone with machine-learning Defcon talk wrap-up的更多相关文章
- Advice for applying Machine Learning
https://jmetzen.github.io/2015-01-29/ml_advice.html Advice for applying Machine Learning This post i ...
- How do I learn machine learning?
https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? ...
- 壁虎书2 End-to-End Machine Learning Project
the main steps: 1. look at the big picture 2. get the data 3. discover and visualize the data to gai ...
- CheeseZH: Stanford University: Machine Learning Ex2:Logistic Regression
1. Sigmoid Function In Logisttic Regression, the hypothesis is defined as: where function g is the s ...
- CheeseZH: Stanford University: Machine Learning Ex1:Linear Regression
(1) How to comput the Cost function in Univirate/Multivariate Linear Regression; (2) How to comput t ...
- 《Learning scikit-learn Machine Learning in Python》chapter1
前言 由于实验原因,准备入坑 python 机器学习,而 python 机器学习常用的包就是 scikit-learn ,准备先了解一下这个工具.在这里搜了有 scikit-learn 关键字的书,找 ...
- 机器学习案例学习【每周一例】之 Titanic: Machine Learning from Disaster
下面一文章就总结几点关键: 1.要学会观察,尤其是输入数据的特征提取时,看各输入数据和输出的关系,用绘图看! 2.训练后,看测试数据和训练数据误差,确定是否过拟合还是欠拟合: 3.欠拟合的话,说明模 ...
- Machine Learning In Action 第二章学习笔记: kNN算法
本文主要记录<Machine Learning In Action>中第二章的内容.书中以两个具体实例来介绍kNN(k nearest neighbors),分别是: 约会对象预测 手写数 ...
- [C2P2] Andrew Ng - Machine Learning
##Linear Regression with One Variable Linear regression predicts a real-valued output based on an in ...
随机推荐
- Python 遍历文件夹清理磁盘案例
import os suffix_name_list = [".pdb", ".ilk"] def find_file(path): # 遍历文件夹 for i ...
- apidoc 工具的使用
使用rest framerok时,需要写API接口文档,此时就需要用到 apidoc(个人觉得这个用的比较顺手) 需要安装nodejs,,, windows 下 1 然后验证是否安装成功 node ...
- MySQL增量备份与恢复实例
小量的数据库可以每天进行完整备份,因为这也用不了多少时间,但当数据库很大时,就不太可能每天进行一次完整备份了,这时候就可以使用增量备份.增量备份的原理就是使用了mysql的binlog日志. 本次操作 ...
- c# 执行调用Oracle Procedure传参及回传值
////定義參數 //IDataParameter[] parameters = // { ...
- 为什么要用BigDecimal
一般货币计算的时候都要用到BigDecimal类,为什么一般不适用float或者double呢? 先看一下浮点数的二进制表示: 小数 0.125 0.125 * 2 = 0.25 0 0.25 * 2 ...
- 图像处理---视频<->图片
图像处理---视频<->图片 // 该程序实现视频和图片的相互转换. // Image_to_video()函数将一组图片合成AVI视频文件. // Video_to_image()函数将 ...
- Subordinates(贪心)
题目大意: 一共有N个员工,其中最高领导人是编号s的人,每个人都只有一个直接领导,每个人都说出了自己领导的个数,问最少有几个人撒谎了. 思路: 合理的贪心是该把排最后的数变成缺少的数字,然后继续判断. ...
- 玩深度学习选哪块英伟达 GPU?有性价比排名还不够!
本文來源地址:https://www.leiphone.com/news/201705/uo3MgYrFxgdyTRGR.html 与“传统” AI 算法相比,深度学习(DL)的计算性能要求,可以说完 ...
- SPFA的优化
[为什么要优化] 关于SPFA,他死了(懂的都懂) 进入正题... 一般来说,我们有三种优化方法. SLF优化: SLF优化,即 Small Label First 策略,使用 双端队列 进行优 ...
- nginx中location的顺序(优先级)及rewrite规则写法
一.location正则写法 一个示例: location = / { # 精确匹配 / ,主机名后面不能带任何字符串 [ configuration A ] } location / { # 因为所 ...