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:

https://youtu.be/ao3P5QCrF_M

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:

  1. They like our research on the game/cards balance and are very enthusiastic and supportive about it.

  2. 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.

  3. 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:

  1. How to price Hearthstone cards: Presents the card pricing model used in the follow-up posts to find undervalued cards.
  2. How to find undervalued cards automatically: Builds on the pricing model to find undervalued cards automatically.
  3. Pricing special cards: Showcases how to appraise the cost of cards that have complex effects, like VanCleef.
  4. Predicting your Hearthstone’s opponent deck: Demonstrates how to use machine learning to predict what the opponent will play.
  5. 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

  1. Advice for applying Machine Learning

    https://jmetzen.github.io/2015-01-29/ml_advice.html Advice for applying Machine Learning This post i ...

  2. 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? ...

  3. 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 ...

  4. 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 ...

  5. 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 ...

  6. 《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 ...

  7. 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 ...

  8. 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 ...

  9. [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

  1. vue,onerror实现当图片加载失败时使用默认图

    1. 2.

  2. [LeetCode] 784. 字母大小写全排列 ☆☆☆(回溯、深度优先遍历)

    https://leetcode-cn.com/problems/letter-case-permutation/solution/shen-du-you-xian-bian-li-hui-su-su ...

  3. Image Processing and Analysis_8_Edge Detection:Edge Detection Revisited ——2004

    此主要讨论图像处理与分析.虽然计算机视觉部分的有些内容比如特 征提取等也可以归结到图像分析中来,但鉴于它们与计算机视觉的紧密联系,以 及它们的出处,没有把它们纳入到图像处理与分析中来.同样,这里面也有 ...

  4. 13_sqoop数据迁移概述

    3. sqoop数据迁移 3.1 概述 sqoop是apache旗下一款“Hadoop体系和关系数据库服务器之间传送数据”的工具. 导入数据:MySQL,Oracle导入数据到Hadoop的HDFS. ...

  5. jade-for-each-while

    if else还是for循环,在jade里面都是可执行的代码 for循环 - var lession = {course:'jade', level:'high'} - for (var k in l ...

  6. ubuntu---记录.简单一句话安装tf

    卸载 sudo pip3 uninstall tensorflow sudo pip3 uninstall tensorflow-gpu sudo pip3 uninstall tensorflow- ...

  7. ndk学习之c++语言基础复习----C++线程与智能指针

    线程 线程,有时被称为轻量进程,是程序执行的最小单元. C++11线程: 我们知道平常谈C++线程相关的东东基本都是基于之后要学习的posix相关的,其实在C++11有自己新式创建线程的方法,所以先来 ...

  8. redis运维相关

    一.redis都有哪些数据类型?分别在哪些场景下使用比较合适?二.redis双写不一致三.雪崩和穿透四.redis的过期策略,LRU五.redis是如何实现高性能高并发六.如何保证Redis的高并发和 ...

  9. vscode开发

    基于 Electron 开发.typescript编写.底层 Node.js 打造的一个编辑器 , 不是IDE,被称为“披着IDE外衣的编辑器”

  10. ansible中roles的简单使用

    一.初识roles 上节中我们已经了解了playbook,详见  https://www.cnblogs.com/wangtaobiu/p/10691689.html 当我们在配置playbook时会 ...