“In this paper specifically, we have tried to predict the [ranking] of the player in the ultimate survival test,” the project’s contributors wrote in a preprint paper (“Survival of the Fittest in PlayerUnknown’s BattleGrounds“) published on Arxiv.org. “We have applied multiple machine learning models to find the optimum prediction.”
Source: AI predicts PUBG player placement from stats and rankings | VentureBeat
Libratus was developed by Computer Science Professor Tuomas Sandholm and his Ph.D. student, Noam Brown. Libratus is being used in this contest to play poker, an imperfect information game that requires the AI to bluff and correctly interpret misleading information to win. Ultimately programs like Libratus also could be used to negotiate business deals, set military strategy or plan a course of medical treatment — all cases that involve complicated decisions based on imperfect information.
Source: CMU AI Is Tough Poker Player | Carnegie Mellon School of Computer Science
After losing with a rock, for example, a player was more likely to play paper in the next round than the “one in three” rule would predict.
This “win-stay lose-shift” strategy is known in game theory as a conditional response – and it may be hard-wired into the human brain, the researchers say
An attacker that controls more than 50% of the network’s computing power can, for the time that he is in control, exclude and modify the ordering of transactions. This allows him to:
- Reverse transactions that he sends while he’s in control. This has the potential to double-spend transactions that previously had already been seen in the block chain.
- Prevent some or all transactions from gaining any confirmations
- Prevent some or all other miners from mining any valid blocks
via Weaknesses – Bitcoin.
With less than 50%, the same kind of attacks are possible, but with less than 100% rate of success. For example, someone with only 40% of the network computing power can overcome a 6-deep confirmed transaction with a 50% success rate.
At Foursquare, we have a simple, first-principles based method of resolving proposed venue attribute updates. We can gauge each Superuser’s voting accuracy based on their performance on honeypots (proposed updates with known answers which are deliberately inserted into the updates queue). Measuring performance and using these probabilities correctly is the key to how we assign points to a Superuser’s vote.
The Math
Let’s make this more concrete with some math.
via The Mathematics of Gamification | Foursquare Engineering Blog.
Automatic rule-balancing systems could cater for a variety of player abilities, too, generating different rules for different players. A game with variations built in can be a great way to have fun without anyone having an unfair advantage …
via AI card game knows how to bend the rules – tech – 04 May 2013 – New Scientist.