分類
市場分析

The Stars Group 看好澳大力亞市場發展

The Stars Group (TSG)日前公布一項數據,2017年第4季線上撲克僅占其收入不到三分之二。該公司
也預計2018年時,這個數字將會降至60%以下。
||||

ULTTE cart
TSG在三月中旬公布,統計至2017年12月31日止的前三個月,該集團收入成長16.1%至3億6,200萬美元。調整後的收
益持平於1.74億美元,但
淨利成長4.7%至4.720萬美元。而全年收入成長13.6%至13.1億美元,調整後收益增長
14.5%至6億美元,淨利潤增長91.3%至2.593億美元,部分歸因於過去一年的非核心資產抛售。
即便退出澳大利亞和哥倫比亞市場,TSG持續減少旗下品牌撲克之星對真錢線上撲克收入的依賴。捷克第四季收入占
比65.1%,略低於前一年的70%,而娛樂場和體營博弈的收入則從25.8%成長至31.2%。
展望2018年,TSG預期營收將落在13.9億至14.7億美元間,調整後盈利將落在6.25億至6.5億美元間。該集團最近也在
澳大利亞的體育博弈收購熱潮中完成一些交易,除吃下了CrownBet大部分股權,也併吞William Hu的澳大利亞分公
司。該公司預計會成為澳大利亞市場第三大的營運商。
此外,TSG並不看好俄羅斯線上博弈市場的經營情況。它們指出俄羅斯的潛在危機將從2018年5月後慢慢浮現,因為俄
羅斯將主辦FIFA世界盃,並承諾對線上博弈營運商進行更嚴厲的打擊,而非持有俄羅斯牌照,包括腹下的撲克之星和長
體育博弈品牌BetStar旨會受到影響。
※本文章屬於TNZE天擇集團所有嚴禁轉載※

分類
人工智慧

撲克玩家的地獄 ? AI強勢來襲 !

人工智慧當道,已成為市場顯學。舉凡任何與網路相關的產業,無不積極投入與開發AI的技術,其中博奕
遊戲產業也深受影響。匹茲堡超級計算中心的研究團隊以AI與四位世界級職業撲克選手展開人腦與電腦的
對決,並在競賽中赢得了170萬美元!本文為您詳細解析,A是如何擊敗四位世界頂尖撲克選手!
slavina Trocco hands of heads-ub. no-lim
“That was anticlimactic,” Jason Les said with a smirk, getting up from his seat. Unlike nearly everyone else in
Pittsburgh’s Rivers Casino, Les had just played his last few hands against an artificially intelligent opponent on
a computer screen. After his fellow players – Daniel McAulay next to him and Jimmy Chou and Dong Kim in an
office upstairs — eventually did the same, they started to commiserate. The consensus: That AI was one hell of a
player.
The four of them had spent the last 20 days playing 120,000 hands of heads-up, no-limit Texas Hold’em
against an artificial intelligence called Libratus created by researchers at Carnegie Mellon University. At stake: a
total pot of $200,000 and, on some level, the pride of the human race. A similar scene had unfolded two years
prior when Les, Kim and two other players decisively laid the smackdown on another Al called Claudico. The
players hoped to put on a repeat performance, finish up the event January 30th, and ride the rush of
endorphins until they got home and resumed their usual games of online poker.
The fight wasn’t even close. All told, Libratus won by more than 1.7 million (virtual) dollars, and — just like that
— the second Brains vs. Al competition came to a close. To understand what these players were up against and
what makes Libratus work, let’s go back to a time before all hope of victory was lost.
Men vs. machine
For the four men playing against Libratus, victory didn’t always seem impossible. The AI was in the lead from
the get-go, building an impressive streak of wins for the first three days. Then came the counter-attack. Day
four saw the gap narrow $40,000, and a string of successes on day six brought the humans to within $50,000 of
the lead.
“In the start here, we lost the first day,” Les explained. “Whatever — not a big deal. And then we were losing,
but then we fought back up to nearly equal. We were feeling really confident! We know how to play, we’re
going to be able to win.”
On the night after the sixth day of competition, the humans did what they did every other night: sift through
the Libratus hand data provided to them by CMU in hopes of devising a winning strategy. With spirits high after
a big day, they decided on a seemingly crazy strategy: three-betting on every hand that came along.
Three-betting, for the uninitiated, is poker slang for reraising on a hand. When you decide to play a hand in a
situation like this, paying the blinds is the first bet. If you’re confident in your cards, you raise — that’s the
second bet. Generally, when you reraise — the third bet-you’re pretty sure you’ve got the exchange in the
bag. Based on their understanding of Libratus’ play style, the humans thought they could knock if off balance
by playing this aggressively for a while. It backfired.
“We applied this crazy strategy we would never do online,” Kim explained. “Basically, we reraised all of our
hands. All of us went in, like, ‘Let’ s just try this, let’ s go crazy.”
“We had a reason to believe that specific size-three-bet was going to work well against the AI,” Les added.
“We just fired off all day doing that.”
Les and Kim concede that they just got unlucky, too, but either way: Libratus was unfazed by their plan and
started demolishing them. “It just kept improving every single day, and we started going backwards and
backwards,” Les said. In fairness, the humans weren’t playing with their usual setups. The four competitors
are almost exclusively online poker pros, and when duking it out at virtual tables at home, they always have
their HUDs handy. These heads-up displays are filled with stats and probabilities that help online players make
the best moves. Their absence here in Pittsburgh was noticeable.
“Without the HUD, without the numbers, you don’t know if you’re being paranoid or not,” Daniel McAulay
said, leaning back in his chair after winning a hand. “Is it folding less? We were never sure. We would always
say the same thing to each other. Just play it out until we get home and we’d see the sample of hands and
then we’ll change the plan. But that cost us a lot of money. A lot of money.”
Those losses would only continue to mount.
Building the beast
One of the men responsible for the players’ anguish can usually be found in his ninth-floor office, overlooking
Carnegie Mellon University’s snow-flecked quad. Professor Tuomas Sandholm might live a second life as a
startup entrepreneur, but he has spent years trying to perfect the algorithms that make Libratus such a potent
player. It wasn’t out of any particular love for the game — Sandholm admits he’s no poker pro — but he was
fascinated by the thought of complex computer systems that make decisions better than we can. That fixation
led him to co-create Claudico (the earlier AI that the humans trounced) with PHD student Noam Brown, and it
led the two of them to try again with Libratus.
To think of Libratus as just a poker-playing champ is to sorely underestimate it. Instead, Sandholm says, it’ sa
more general set of algorithms meant to tackle any information-imperfect situation. Confused? Don’t be.
Broadly speaking, the term just describes any situation in which two or more parties don’t have the same
information. Something unlike, say, chess, where the entirety of the game’ s world is splayed out on the board
in front of players. Those players can figure out exactly what’s going on and, assuming they have decent
memories, draw on their understanding of the events that led them there. This is a perfect information game.
No-limit Texas Hold’em is different. You don’t know which cards your opponent has, your opponent
doesn’t know which cards you have, and those minutes playing a hand to its conclusion are spent trying to
make the smartest moves possible with a shortage of intel. And unlike the limit variant, where there’s a cap on
how big your bets can be, no-limit gives you the freedom to bet whatever you want. There’s so much
information a person — or an AI — can infer about an opponent’ s strategy based on their bets that it sno
wonder researchers have been trying to crack the game.
“Heads-up, no-limit Texas Hold’ em poker has emerged as the leading benchmark for measuring the quality
of these general purpose algorithms in the AI community,” Sandholm told me.
With that in mind, Sandholm and Brown jointly built Libratus from three major components. The first is an
algorithm that devises overall strategies based on Nash equilibria. In other words, Libratus spent a total of 15
million computing hours chewing on the rules of the game before the competition, finding rational ways to act
when both players are making the best possible moves with the information available. Thanks to a new logic
model developed by the two researchers to minimize Libratus’ “regret,” the AI could solve larger
abstractions of the game faster and with higher accuracy than before.
The second is what Sandholm calls the end-game solver. This is the part that players actually faced during their
20 days of combat. Unsurprisingly, too, this is where Sandholm says most innovative breakthroughs have
happened. Essentially, this allowed Libratus to cook up an approach based on the first two cards it was dealt,
and modify that approach based on its opponent’ s actions and the river and flop that are dealt. Sandholm
says Libratus was also designed to keep tabs on how safe its options are. Let’s say a human player screws up
and loses $372. That money is viewed as a gift of sorts, so the AI can freely lose up to $372 and still remain
ahead.
ULTTE Garming
“That gives us more flexibility for optimizing our strategies while still being safe,” Sandholm explained.
We’ II get to the last key component a little later. In any case, the sheer number of complex calculations meant
Libratus couldn’t run on the desktop in Sandholm’ s office. If nothing else, the human players can take solace
in the fact that it took a supercomputer and millions of computing hours to beat them. If you thought Gowas
tough to wrap your head around, consider the complexity of no-limit Texas Hold’em: When you’re dealt into
a game, the hands you’re dealt and the communal cards that appear are one possibility of 10^160.
“That’s one followed by 160 zeroes,” said Sandholm. “That’s more than the number of atoms in the
universe. You cannot just brute-force your way through it.” Still, it takes some degree of brute force to build as
close to optimal a strategy as possible. That’s where “Bridges” comes in.
If Libratus is the brain of the operation, Bridges — a supercomputer made of hundreds of nodes in the
basement of the Pittsburgh Supercomputing Center – is most definitely the brawn.
“Libratus is running on about 600 nodes at Bridges, out of 846 total compute nodes,” said Nick Nystrom,
senior director of research at the Pittsburgh Supercomputing Center. Most of those 800+ nodes have two
CPUs, each with 28 computing cores and 128GB of RAM. Forty-eight of those nodes have two state-of-the-art
GPUs, and still others were loaded with even more power: NVIDIA’ s Tesla-series K80 and P100 GPUs.
༧.༠༠
There’s more: 42 of those nodes have 3TB of RAM each, and a very special four nodes have a whopping 12TB
of RAM. That’s some serious firepower, but all those nodes were ingeniously woven together to maximize
data bandwidth and minimize latency. It’s just as well, considering the amount of data involved: Libratus was
using up to 2.6 petabytes of storage during the competition.
When not being used to best humans at card games, Bridges was being used for around 650 projects by more
than 2,500 people. Think of Bridges as a supercomputer for hire: Researchers from around the country are using
it to gain insight into arcane subjects like genomics, genome sequence assemblies and other kinds of machine-
learning.
The beauty of Bridges, according to Nystrom, is that those researchers don’t need to be supercomputer buffs.
“It’ sa very cloud-like model letting people who are not programmers, not computer scientists, not
supercomputer users make use of a supercomputer without necessarily even knowing it.” That’ s what
happened with Libratus, and everything seemed to be working perfectly.
ULTS 2 Garmin
GATING
Game theory
After the humans’ gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual
winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but
the humans were feeling worn out. When I chatted with Kim and Les in their hotel bar after the penultimate
day’s play, the mood was understandably somber.
“Yesterday, I think, I played really bad,” Kim said, rubbing his eyes. “I was pretty upset, and I made a lot of
big mistakes. I was pretty frustrated. Today, I cut that deficit in half, but it’s still probably unlike for me to
win.” At this point, with so little time left and such a large gap to close, their plan was to blitz through the
remaining hands and complete the task in front of them.
For these world-class players, beating Libratus had gone from being a real possibility to a pipe dream in just a
matter of days. It was obvious that the AI was getting better at the game over time, sometimes by leaps and
bounds that left Les, Kim, McAulay and Chou flummoxed. It wasn’t long before the pet theories began to
surface. Some thought Libratus might have been playing completely differently against each of them, and
others suspected the AI was adapting to their play styles while they were playing. They were wrong.
As it turned out, they weren’t the only ones looking back at the past day’ s events to concoct a game plan for
the days to come. Every night, after the players had retreated to their hotel rooms to strategize, the basement
of the Supercomputing Center continued to thrum. Libratus was busy. Many of us watching the events unfold
assumed the AI was spending its compute cycles figuring out ways to counter the players’ individual play
styles and fight back, but Professor Sandholm was quick to rebut that idea. Libratus isn’t designed to find
better ways to attack its opponents; it’ s designed to constantly fortify its defenses. Remember those major
Libratus components I mentioned? This is the last, and perhaps most important, one.
“All the time in the background, the algorithm looks at what holes the opponents have found in our strategy
and how often they have played those,” Sandholm told me. “It will prioritize the holes and then compute
better strategies for those parts, and we have a way of automatically gluing those fixes into the base strategy.”
If the humans leaned on a particular strategy — like their constant three-bets — Libratus could theoretically
take some big losses. The reason those attacks never ended in sustained victory is because Libratus was quietly
patching those holes by using the supercomputer in the background. The Great Wall of Libratus was only one
reason the AI managed to pull so far ahead. Sandholm refers to Libratus as a “balanced” player that uses
randomized actions to remain inscrutable to human competitors. More interesting, though, is how good
Libratus was at finding rare edge cases in which seemingly bad moves were actually excellent ones.
“It plays these weird bet sizes that are typically considered really bad moves,” Sandholm explained. These
include tiny underbets, like 10 percent of the pot, or huge overbets, like 20 times the pot. Donk betting, limping
— all sorts of strategies that are, according to the poker books and folk wisdom, bad strategies.” To the
players’ shock and dismay, those “bad strategies” worked all too well.
Poker and beyond
On the afternoon of January 30th, Libratus officially won the second Brains vs AI competition. The final margin
of victory: $1,766,250. Each of the players divvied up their $200,000 spoils (Dong kim lost the least amount of
money to Libratus, earning about $75,000 for his efforts), fielded questions from reporters and eventually left to
decompress. Not much had gone their way over the past 20 days, but they just might have contributed to a
more thoughtful, Al-driven future without even realizing it.
Through Libratus, Sandholm had proved algorithms could make better, more-nuanced decisions than humans
in one specific realm. But remember: Libratus and systems like it are general-purpose intelligences, and
Sandholm sees plenty of potential applications. As an entrepreneur and negotiation buff, he’ s enthusiastic
about algorithms like Libratus being used for bargaining and auctions.
“When the FCC auctions spectrum licenses, they sell tens of billions of dollars of spectrum per auction, yet
nobody knows even one rational way of bidding,” he said. “Wouldn’t it be nice if you had some AI
support?”
But there are bigger problems to tackle – ones that could affect all of us more directly. Sandholm pointed to
developments in cybersecurity, military settings and finance. And, of course, there’s medicine.
“In a new project, we’re steering evolution and biological adaptation to battle viral and bacterial infections,”
he said. “Think of the infection as the opponent and you’re taking sequential actions and measurements just
like in a game.” Sandholm also pointed out that such algorithms could even be used to more helpfully
manage diseases like cancer, both by optimizing the use of existing treatment methods and maybe even
developing new ones.
Jason, Dong, Daniel and Jimmy might have lost this prolonged poker showdown, but what Sandholm, Brown
and their contemporaries have learned in the process could lead to some big wins for humanity.

分類
歐美訊息篇

線上撲克MPN進攻拉丁美洲市場

博彩遊戲網站Tower Torneos與Microgaming旗下專慶撲克平台MPN聯手,藉由整合雙方線上撲克平台
的策略,準備拓展拉丁美洲線上市場。
線上遊戲開發商Microgaming於官網上宣布,與博彩遊戲網站Tower Torneos正式成為合作夥伴。根據合作策略,除
了線上撲克室之外,Tower Torneos還會在MPN (Microgaming Poker Network)提供體育博彩、賓果等其他熱門博
彩遊戲。並協議将拓展目標設定在拉丁美洲的線上市場。
aming Poker Network) RAB
日前Microgaming透過與Quickfire的平台整合計畫,獲得了更精緻、多元的遊戲內容與方案。對此,Tower Torneos
運營總監Horacio Santoianni
表示:「與Microgaming的合作是一件非常愉快的事情,他們與Quickfire整合的成功告
訴我們,他們是一個追求卓越創新的完美彩伴。他們在撲克業界擁有豐富的經驗,並具備遊戲開發的靈活性。」
透過MPN平台,合作的運營商與玩家們能夠享有一個獨立且快速的網絡,MPN将提供全面性錦標賽賽程,以及各種最
受歡迎的現場賽事直播(the MPNPT):
Microgaming網絡遊戲總監Jean-LucFeriere表示:「我們很歡迎Tower Torneos加入MPN,並成為他們的供應商。
Tower Torneos的加入將增加我們在網絡市場上的整體流動性,同時也增加我們在歐洲以外市場的市佔率。 Tower
Torneos的團隊為MPN帶來豐富的產業和撲克經驗,我們很高興能與他們長期合作。

※本文章屬於TNZE天擇集團所有嚴禁轉載※

 

 

 

 

分類
歐美訊息篇

888Poker準備在義大利大展拳腳

在英國上市的線上博弈品牌888 Holdings,已於2018年正式在義大利市場推出其風靡全球的線上撲克網
站。2017年9月,888執行長就宣布旗下產品將登陸義大利,而今年初即履行其宣言。
ULTI & Gaming
目前,義大利玩家可以透過iOS和Android的移動應用程式場玩888旗下的撲克產品,888執行長ltai Pazner也提醒使用
桌機的玩家,透過移動裝置和設備如:智慧型手機和平板,即可再也不受時空限制隨心所欲地爽玩888的全系列產品。
888也趁勢推出一系列全新美金營銷活動,並以I Gioco inizia adesso ! 讓遊戲開始)為口號,在各大電視頻道強力放送
宣傳!
除此之外,888也领世界撲克系列達成協議,允許888poker.it的客戶有機會贏得参加拉斯維加斯年度錦標賽的優惠,包
括:旅費和錦標賽報名費。
而當888如火如荼地展開在義大利撲克市場布著時,歐洲線上撲克世界仍沉浸於法國、義大利葡萄牙和西班牙等監管
機構已同意分享線上撲克的流動性框架,這項政策被視為拯救垂死撲克市場的解禁。
而這項政策允許各自線上撲克市場持牌營運商,包括法國和西班牙已率先加入這個共享池中,但義大利受政府擔憂流動
性協議將造成跨境洗錢氾濫仍躅不前。預計這個情況在2018年義大利3月大選前幾個月,政府尚不會碰賴此一議題。

※本文章屬於TNZE天擇集團所有嚴禁轉載※

分類
歐美訊息篇

知名老虎機開發商Play`n GO攜手撲克之星開拓市場

Play’nG0日前與The Stars Group旗下撲克之星簽署一份為期多年的合作協議,將透過整合的方式提供
自家所有的遊戲至撲克之星的平台上,重點在於擴大其產品市佔率。
屢獲殊榮的老虎機專家 Play nGO日前與The Stars Group旗下撲克之星簽署一份為期多年的合作協議。該協議內容指
出Play nGO將透過整合的方式提供自家所有的遊戲至撲克之星的平台上,重點在於擴大產品市佔率。
近幾個月來, Play nGO的遊戲產品已在幾個新的管轄區通過遊戲認證,且這一最新協議將允許Play’n GO進一步將
市場拓展至義大利、西班牙、丹麥和英國。未來還計劃進軍至葡萄牙及羅馬尼亞。 TL-
Play nGO執行長JohanTörnavist表示:「The Stars Group近年來看到撲克之星大幅成長,我們很高興能夠將我們的
遊戲添加到他們的平台中。我們的遊戲已通過15國以上的多項認證,也證言著我們的產品一直是業界的首選。」
Play’n GO銷售總監Magnus Olsson表示:「Play nGO整合的過程高定順利,我非常有信心,我們與撲克之星能保
持長期合作。」
撲克之星董事總經理BoWanghammar表示:「撲克之星致力於改善复遊戲內容,包括接入第三方遊戲。我們非常期待
與Play n GO合作,為我們的平台,無論是桌機還是手機玩家提供精彩的遊戲。

※本文章屬於TNZE天擇集團所有嚴禁轉載※

 

 

 

 

 

 

 

分類
歐美訊息篇

Microgaming推出全新的撲克用戶端

Microgaming (MG)於上月26日在MPN平台上推出名為Prima的全新在線撲克用戶端,旨在為全球玩家
提供更優質的體驗。
Microgaming (MG)於上月26日在MPN平台上推出名為Prima的全新在線撲克用戶端,旨在為全球玩家提供更優質
的體驗。
新的用戶端完全以客為中心設計,簡約的介面、明瞭的資金動向、各式錦標賽及賭場遊戲,其中還包括德州撲克的遊戲
大廳及簡單易懂的笑勵系統。
ULTTE Garmine
在Prima用戶端上,玩家可透過歷史記錄查詢工具,篩選任何時段遊戲的盈虧,並可利用此功能查看過去遊玩的方式。
Prima用戶端採模塊化的方式設計,並採用相同的代碼庫,使MG能夠同時在所有平台上快速部署且更新。這項新技術
的優勢是,除了支援Mircosof所有設備外,只要有新的功能一推出,用戶即可馬上知道並可立即使用。
MG的新撲克用戶端採用高解析度的遊戲畫面,為玩家提供卓越的遊戲分辨率。此外,還提供一個有趣的功能,玩家可
以將遊玩的歷史畫面轉成GIF檔,隨時可上傳社群媒體與他人分享。
MG的CCO Jean-Luc Ferriere評論道:「對於Microgaming Poker Network來說,這是一個激動人心的時刻。 Prima
的推出象徵著我們在遊戲領域的發展上又更上一層樓,我們提供更直覺、更簡約的用戶端為玩家提供最佳的撲克體驗,
並針對任何設備進行優化。」
Microgaming產品負責人Alex Scott補充說:「這是我們多年戰略的結晶,目的在精進我們的產品並超越競爭對手。我
為團隊的成就感到相當自豪,未來能和團隊共同為此專秦奮鬥,我感到非常興奮。」

※本文章屬於TNZE天擇集團所有嚴禁轉載※

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

分類
亞洲動態

微賽布局智力競技 攜手中國撲克遊戲嘉年華

2008年開始舉辦的CPC中國撲克遊戲嘉年華(China Poker Carnival),是中國首個大型線下棋牌賽事,
也是海南省重大文體賽事之一。該賽事的成功,帶動中國線下大型撲克活動的舉辦。該賽事合作夥伴微賽
棋牌也著手布局智力競技賽事,強強聯合推動中國棋牌遊戲線上與線下的結合推廣,打造中國棋牌遊戲新
氣象。
手選拔工作的同時
中國撲克遊戲嘉年華,作為國內最具規模的撲克盛宴即將再次拉開帷幕。本屆CPC將於10月1日黃金周在海南省三亞市
海棠灣盛大開賽,作為海南又一重大文化體育賽事,吸引了海內外諸多撲克高手的關注。微妻在棋牌界已不算新面孔,
本次,微妻作為賽事戰略合作夥伴。通過自身管道,為妻事提供參賽選手選拔工作的同時,還將為賽事提供了品牌推廣
及賽事宣傳服務。
CPC中國撲克遊戲嘉年華是中國首個大型線下棋牌賽事,也是海南省的重大文體賽事之一。嘉年華的成功,還帶動了國
內線下大型撲克活動的舉辦。今年,作為第五屆中國撲克遊戲嘉年華活動,主辦方開出了極其具有誘惑力的奖金額度,
屆時將邀請600位選手爭奪1111萬元總獎金,冠軍將榮獲300萬獎勵,更將額外獲得一枚由500克24K黃金製成的冠軍
奖牌。同時,作為賽事戰略合作夥伴,微賽更将依靠自身優勢,及自有行業影響力,全力為第五屆賽事提供宣傳服務,
打通多種管道,為赛事宣傳推廣工作提供支援。
此前,微養早已與大型國際賽事在行銷、票務等領域的深度合作,並已成功覆蓋體育賽事中80%的項目,對頭部賽事集
中資源成功實現行銷推廣。在傳統競技項目具有一定影響力後,自2016年起,微賽開始不斷著手佈局智力競技賽事。
成功合作過全球頂級WPT賽事,也成功舉辦過自有赛事及全國鬥地主錦標賽。從線上賽事推震,到線下賽事落地,微賽
團隊憑藉豐富的經驗,都可一舉完成。
同時,微賽在擁有微信、QQ賽事入口獨家運營權的同時,近期又新增了三大互雅網核心流量入口,包括美國入口、大
眾點評入口、貓眼入口。憑藉優勢流量入口積累的大量用戶資源,微賽還將運用大資料與社交網路,實現體育行業資料
+ 體育使用者資料的深度融合。尋找到最具針對性的用戶人群,進行精准推送,進一步強化賽事的推震及傳播工作。
雙方的此番合作可謂是強強聯合,為海內外的眾多智力競技愛好者們
提供了一個技藝切磋、交流的大平臺。微賽助力賽
事能夠在國內得到進一步發展的同時,也在為中國棋牌行業綠色、健康、有序的可持續發展奉獻了堅實的中堅力量。未
來,微賽還將與眾多國內外頂級智力競技賽事開展合作,並開拓出更加多樣的行銷合作新模式。

※本文章屬於TNZE天擇集團所有嚴禁轉載※

 

 

分類
亞洲動態

Pokeryaar加入Microgaming欲成為印度最大撲克網站

Pokeryan.com Microgaming展開合作,聯手為印度線上玩家研發在地化的用戶界面與服務內容。有意
在印度發展成為當地最大線上撲克平台。
專家指出,印度的手機軟體市場將超越美國,下一波網路行業淘金潮就在印度!近年來印度的網路發展備受關注,不僅
是網路用戶量,還有與互聯網相關的消費都穩定上升中。如今,印度已成為各產業,特別是互聯網行業搶占商機的新去
有針對印度互聯網市場進行分析的報告顯示,2016年印度地區 Google 應用商店的下載量已經追上了美國,而且保持
著上升趨勢。其中線上遊戲等項目更是在印度當地特別熱門。

乘著這股熱潮,線上撲克網站Pokeryaar宣布,已加入由Microgaming領軍的印度撲克線上市場。他們將聯手為印度線
上玩家設計在地化的用戶界面及服務內容,目標成為印度最大的線上撲克平台。
Pokeryaar.com透過線上下載,提供廣大玩家最即時的撲克遊戲,並支援手機App等應用程式。其商業總監Aman
Shahpuri表示:「Pokeryaar.com將徹底改變印度的遊戲行業。憑藉先進的技術和專為印度市場定制的產品,我們的目
標是把撲克變成主流,並為我們的目標客群提供一個優秀的娛樂平台。我們希望成為印度第一大線上撲克平台。
Microgaming的合作,他們的世界級撲克產品有助於我們更卓越。」
Pokenyaar.com是Microgaming在印度線上撲克市場上第四個合作的撲克運營商。Microgaming遊戲總監Jean Luc
Ferriere對這項發展發表評論:「我們很歡迎多一個充滿活力的年輕品牌加入我們在印度的線上撲克市場,近年來,我
們在網路領域取得了重大進展,特別是2017年的產品和運營方面更是達到高峰。2018年我們只承諾做更大更好的填
目。我們看待這個重要領域及市場的前景非常樂觀,現在就是進場的好時機。」
※本文章屬於TNZE天擇集團所有嚴禁轉載※

分類
人工智慧

撲克玩家的地獄 ? AI強勢來襲 !

人工智慧當道,已成為市場顯學。舉凡任何與網路相關的產業,無不積極投入與開發AI的技術,其中博奕
遊戲產業也深受影響。匹茲堡超級計算中心的研究團隊以AI與四位世界級職業撲克選手展開人腦與電腦的
對決,並在競賽中赢得了170萬美元!本文為您詳細解析,A是如何擊敗四位世界頂尖撲克選手!
slavina Trocco hands of heads-ub. no-lim
“That was anticlimactic,” Jason Les said with a smirk, getting up from his seat. Unlike nearly everyone else in
Pittsburgh’s Rivers Casino, Les had just played his last few hands against an artificially intelligent opponent on
a computer screen. After his fellow players – Daniel McAulay next to him and Jimmy Chou and Dong Kim in an
office upstairs — eventually did the same, they started to commiserate. The consensus: That AI was one hell of a
player.
The four of them had spent the last 20 days playing 120,000 hands of heads-up, no-limit Texas Hold’em
against an artificial intelligence called Libratus created by researchers at Carnegie Mellon University. At stake: a
total pot of $200,000 and, on some level, the pride of the human race. A similar scene had unfolded two years
prior when Les, Kim and two other players decisively laid the smackdown on another Al called Claudico. The
players hoped to put on a repeat performance, finish up the event January 30th, and ride the rush of
endorphins until they got home and resumed their usual games of online poker.
The fight wasn’t even close. All told, Libratus won by more than 1.7 million (virtual) dollars, and — just like that
— the second Brains vs. Al competition came to a close. To understand what these players were up against and
what makes Libratus work, let’s go back to a time before all hope of victory was lost.
Men vs. machine
For the four men playing against Libratus, victory didn’t always seem impossible. The AI was in the lead from
the get-go, building an impressive streak of wins for the first three days. Then came the counter-attack. Day
four saw the gap narrow $40,000, and a string of successes on day six brought the humans to within $50,000 of
the lead.
“In the start here, we lost the first day,” Les explained. “Whatever — not a big deal. And then we were losing,
but then we fought back up to nearly equal. We were feeling really confident! We know how to play, we’re
going to be able to win.”
On the night after the sixth day of competition, the humans did what they did every other night: sift through
the Libratus hand data provided to them by CMU in hopes of devising a winning strategy. With spirits high after
a big day, they decided on a seemingly crazy strategy: three-betting on every hand that came along.
Three-betting, for the uninitiated, is poker slang for reraising on a hand. When you decide to play a hand in a
situation like this, paying the blinds is the first bet. If you’re confident in your cards, you raise — that’s the
second bet. Generally, when you reraise — the third bet-you’re pretty sure you’ve got the exchange in the
bag. Based on their understanding of Libratus’ play style, the humans thought they could knock if off balance
by playing this aggressively for a while. It backfired.
“We applied this crazy strategy we would never do online,” Kim explained. “Basically, we reraised all of our
hands. All of us went in, like, ‘Let’ s just try this, let’ s go crazy.”
“We had a reason to believe that specific size-three-bet was going to work well against the AI,” Les added.
“We just fired off all day doing that.”
Les and Kim concede that they just got unlucky, too, but either way: Libratus was unfazed by their plan and
started demolishing them. “It just kept improving every single day, and we started going backwards and
backwards,” Les said. In fairness, the humans weren’t playing with their usual setups. The four competitors
are almost exclusively online poker pros, and when duking it out at virtual tables at home, they always have
their HUDs handy. These heads-up displays are filled with stats and probabilities that help online players make
the best moves. Their absence here in Pittsburgh was noticeable.
“Without the HUD, without the numbers, you don’t know if you’re being paranoid or not,” Daniel McAulay
said, leaning back in his chair after winning a hand. “Is it folding less? We were never sure. We would always
say the same thing to each other. Just play it out until we get home and we’d see the sample of hands and
then we’ll change the plan. But that cost us a lot of money. A lot of money.”
Those losses would only continue to mount.
Building the beast
One of the men responsible for the players’ anguish can usually be found in his ninth-floor office, overlooking
Carnegie Mellon University’s snow-flecked quad. Professor Tuomas Sandholm might live a second life as a
startup entrepreneur, but he has spent years trying to perfect the algorithms that make Libratus such a potent
player. It wasn’t out of any particular love for the game — Sandholm admits he’s no poker pro — but he was
fascinated by the thought of complex computer systems that make decisions better than we can. That fixation
led him to co-create Claudico (the earlier AI that the humans trounced) with PHD student Noam Brown, and it
led the two of them to try again with Libratus.
To think of Libratus as just a poker-playing champ is to sorely underestimate it. Instead, Sandholm says, it’ sa
more general set of algorithms meant to tackle any information-imperfect situation. Confused? Don’t be.
Broadly speaking, the term just describes any situation in which two or more parties don’t have the same
information. Something unlike, say, chess, where the entirety of the game’ s world is splayed out on the board
in front of players. Those players can figure out exactly what’s going on and, assuming they have decent
memories, draw on their understanding of the events that led them there. This is a perfect information game.
No-limit Texas Hold’em is different. You don’t know which cards your opponent has, your opponent
doesn’t know which cards you have, and those minutes playing a hand to its conclusion are spent trying to
make the smartest moves possible with a shortage of intel. And unlike the limit variant, where there’s a cap on
how big your bets can be, no-limit gives you the freedom to bet whatever you want. There’s so much
information a person — or an AI — can infer about an opponent’ s strategy based on their bets that it sno
wonder researchers have been trying to crack the game.
“Heads-up, no-limit Texas Hold’ em poker has emerged as the leading benchmark for measuring the quality
of these general purpose algorithms in the AI community,” Sandholm told me.
With that in mind, Sandholm and Brown jointly built Libratus from three major components. The first is an
algorithm that devises overall strategies based on Nash equilibria. In other words, Libratus spent a total of 15
million computing hours chewing on the rules of the game before the competition, finding rational ways to act
when both players are making the best possible moves with the information available. Thanks to a new logic
model developed by the two researchers to minimize Libratus’ “regret,” the AI could solve larger
abstractions of the game faster and with higher accuracy than before.
The second is what Sandholm calls the end-game solver. This is the part that players actually faced during their
20 days of combat. Unsurprisingly, too, this is where Sandholm says most innovative breakthroughs have
happened. Essentially, this allowed Libratus to cook up an approach based on the first two cards it was dealt,
and modify that approach based on its opponent’ s actions and the river and flop that are dealt. Sandholm
says Libratus was also designed to keep tabs on how safe its options are. Let’s say a human player screws up
and loses $372. That money is viewed as a gift of sorts, so the AI can freely lose up to $372 and still remain
ahead.
ULTTE Garming
“That gives us more flexibility for optimizing our strategies while still being safe,” Sandholm explained.
We’ II get to the last key component a little later. In any case, the sheer number of complex calculations meant
Libratus couldn’t run on the desktop in Sandholm’ s office. If nothing else, the human players can take solace
in the fact that it took a supercomputer and millions of computing hours to beat them. If you thought Gowas
tough to wrap your head around, consider the complexity of no-limit Texas Hold’em: When you’re dealt into
a game, the hands you’re dealt and the communal cards that appear are one possibility of 10^160.
“That’s one followed by 160 zeroes,” said Sandholm. “That’s more than the number of atoms in the
universe. You cannot just brute-force your way through it.” Still, it takes some degree of brute force to build as
close to optimal a strategy as possible. That’s where “Bridges” comes in.
If Libratus is the brain of the operation, Bridges — a supercomputer made of hundreds of nodes in the
basement of the Pittsburgh Supercomputing Center – is most definitely the brawn.
“Libratus is running on about 600 nodes at Bridges, out of 846 total compute nodes,” said Nick Nystrom,
senior director of research at the Pittsburgh Supercomputing Center. Most of those 800+ nodes have two
CPUs, each with 28 computing cores and 128GB of RAM. Forty-eight of those nodes have two state-of-the-art
GPUs, and still others were loaded with even more power: NVIDIA’ s Tesla-series K80 and P100 GPUs.
༧.༠༠
There’s more: 42 of those nodes have 3TB of RAM each, and a very special four nodes have a whopping 12TB
of RAM. That’s some serious firepower, but all those nodes were ingeniously woven together to maximize
data bandwidth and minimize latency. It’s just as well, considering the amount of data involved: Libratus was
using up to 2.6 petabytes of storage during the competition.
When not being used to best humans at card games, Bridges was being used for around 650 projects by more
than 2,500 people. Think of Bridges as a supercomputer for hire: Researchers from around the country are using
it to gain insight into arcane subjects like genomics, genome sequence assemblies and other kinds of machine-
learning.
The beauty of Bridges, according to Nystrom, is that those researchers don’t need to be supercomputer buffs.
“It’ sa very cloud-like model letting people who are not programmers, not computer scientists, not
supercomputer users make use of a supercomputer without necessarily even knowing it.” That’ s what
happened with Libratus, and everything seemed to be working perfectly.
ULTS 2 Garmin
GATING
Game theory
After the humans’ gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual
winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but
the humans were feeling worn out. When I chatted with Kim and Les in their hotel bar after the penultimate
day’s play, the mood was understandably somber.
“Yesterday, I think, I played really bad,” Kim said, rubbing his eyes. “I was pretty upset, and I made a lot of
big mistakes. I was pretty frustrated. Today, I cut that deficit in half, but it’s still probably unlike for me to
win.” At this point, with so little time left and such a large gap to close, their plan was to blitz through the
remaining hands and complete the task in front of them.
For these world-class players, beating Libratus had gone from being a real possibility to a pipe dream in just a
matter of days. It was obvious that the AI was getting better at the game over time, sometimes by leaps and
bounds that left Les, Kim, McAulay and Chou flummoxed. It wasn’t long before the pet theories began to
surface. Some thought Libratus might have been playing completely differently against each of them, and
others suspected the AI was adapting to their play styles while they were playing. They were wrong.
As it turned out, they weren’t the only ones looking back at the past day’ s events to concoct a game plan for
the days to come. Every night, after the players had retreated to their hotel rooms to strategize, the basement
of the Supercomputing Center continued to thrum. Libratus was busy. Many of us watching the events unfold
assumed the AI was spending its compute cycles figuring out ways to counter the players’ individual play
styles and fight back, but Professor Sandholm was quick to rebut that idea. Libratus isn’t designed to find
better ways to attack its opponents; it’ s designed to constantly fortify its defenses. Remember those major
Libratus components I mentioned? This is the last, and perhaps most important, one.
“All the time in the background, the algorithm looks at what holes the opponents have found in our strategy
and how often they have played those,” Sandholm told me. “It will prioritize the holes and then compute
better strategies for those parts, and we have a way of automatically gluing those fixes into the base strategy.”
If the humans leaned on a particular strategy — like their constant three-bets — Libratus could theoretically
take some big losses. The reason those attacks never ended in sustained victory is because Libratus was quietly
patching those holes by using the supercomputer in the background. The Great Wall of Libratus was only one
reason the AI managed to pull so far ahead. Sandholm refers to Libratus as a “balanced” player that uses
randomized actions to remain inscrutable to human competitors. More interesting, though, is how good
Libratus was at finding rare edge cases in which seemingly bad moves were actually excellent ones.
“It plays these weird bet sizes that are typically considered really bad moves,” Sandholm explained. These
include tiny underbets, like 10 percent of the pot, or huge overbets, like 20 times the pot. Donk betting, limping
— all sorts of strategies that are, according to the poker books and folk wisdom, bad strategies.” To the
players’ shock and dismay, those “bad strategies” worked all too well.
Poker and beyond
On the afternoon of January 30th, Libratus officially won the second Brains vs AI competition. The final margin
of victory: $1,766,250. Each of the players divvied up their $200,000 spoils (Dong kim lost the least amount of
money to Libratus, earning about $75,000 for his efforts), fielded questions from reporters and eventually left to
decompress. Not much had gone their way over the past 20 days, but they just might have contributed to a
more thoughtful, Al-driven future without even realizing it.
Through Libratus, Sandholm had proved algorithms could make better, more-nuanced decisions than humans
in one specific realm. But remember: Libratus and systems like it are general-purpose intelligences, and
Sandholm sees plenty of potential applications. As an entrepreneur and negotiation buff, he’ s enthusiastic
about algorithms like Libratus being used for bargaining and auctions.
“When the FCC auctions spectrum licenses, they sell tens of billions of dollars of spectrum per auction, yet
nobody knows even one rational way of bidding,” he said. “Wouldn’t it be nice if you had some AI
support?”
But there are bigger problems to tackle – ones that could affect all of us more directly. Sandholm pointed to
developments in cybersecurity, military settings and finance. And, of course, there’s medicine.
“In a new project, we’re steering evolution and biological adaptation to battle viral and bacterial infections,”
he said. “Think of the infection as the opponent and you’re taking sequential actions and measurements just
like in a game.” Sandholm also pointed out that such algorithms could even be used to more helpfully
manage diseases like cancer, both by optimizing the use of existing treatment methods and maybe even
developing new ones.
Jason, Dong, Daniel and Jimmy might have lost this prolonged poker showdown, but what Sandholm, Brown
and their contemporaries have learned in the process could lead to some big wins for humanity.