Collaborating Authors


Human strategic decision making in parametrized games Artificial Intelligence

Strong algorithms have been developed for game classes with many elements of complexity. For example, algorithms were recently able to defeat human professional players in 2-player [16, 3] and 6-player no-limit Texas hold'em [4]. These games have imperfect information, sequential actions, very large state spaces, and the latter has more than two players (solving multiplayer games is more challenging than two-player zero-sum games from a complexity-theoretic perspective). However, these algorithms all require an extremely large amount of computational resources for offline and/or online computations and for optimizing neural network hyperparameters. The algorithms also have a further limitation in that they are using all these resources just to solve for one very specific version of the game (e.g., Libratus and DeepStack assumed that all players start the hand with 200 times the big blind, and Pluribus assumed that all players start the hand with 100 times the big blind).

ELO System for Skat and Other Games of Chance Artificial Intelligence

Assessing the skill level of players to predict the outcome and to rank the players in a longer series of games is of critical importance for tournament play. Besides weaknesses, like an observed continuous inflation, through a steadily increasing playing body, the ELO ranking system, named after its creator Arpad Elo, has proven to be a reliable method for calculating the relative skill levels of players in zero-sum games. The evaluation of player strength in trick-taking card games like Skat or Bridge, however, is not obvious. Firstly, these are incomplete information partially observable games with more than one player, where opponent strength should influence the scoring as it does in existing ELO systems. Secondly, they are game of both skill and chance, so that besides the playing strength the outcome of a game also depends on the deal. Last but not least, there are internationally established scoring systems, in which the players are used to be evaluated, and to which ELO should align. Based on a tournament scoring system, we propose a new ELO system for Skat to overcome these weaknesses.

Facebook Open Sources ReBeL, a New Reinforcement Learning Agent - KDnuggets


I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Poker has been considered by many the core inspiration for the formalization of game theory. John von Neuman was reportedly an avid poker fan and use many analogies of the card game while creating the foundation of game-theory.

AI Algorithm From Facebook Can Play Chess & Poker With Equal Ease


In recent news, the research team at Facebook has introduced a general AI bot, ReBeL that can play both perfect information, such as chess and imperfect information games like poker with equal ease, using reinforcement learning. As the company says, it is a big step towards creating a general AI algorithm that could perform well over a range of games. The researchers believe that this algorithm will have real-world applications, including dealing with negotiations, fraud detection, and even cybersecurity. AlphaZero from DeepMind rapidly caught the fancy of the AI research community when it was released back in 2017. An AI-based program that could play games like chess, shogi, and Go is not unheard of, but AlphaZero is different as it uses reinforcement learning with search (RL Search) to'learn on its own' by mimicking the world-class players.

Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games Artificial Intelligence

We focus on the problem of finding an optimal strategy for a team of two players that faces an opponent in an imperfect-information zero-sum extensive-form game. Team members are not allowed to communicate during play but can coordinate before the game. In that setting, it is known that the best the team can do is sample a profile of potentially randomized strategies (one per player) from a joint (a.k.a. correlated) probability distribution at the beginning of the game. In this paper, we first provide new modeling results about computing such an optimal distribution by drawing a connection to a different literature on extensive-form correlation. Second, we provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile. We can also cap the number of such profiles we allow in the solution. This begets an anytime algorithm by increasing the cap. We find that often a handful of well-chosen such profiles suffices to reach optimal utility for the team. This enables team members to reach coordination through a relatively simple and understandable plan. Finally, inspired by this observation and leveraging theoretical concepts that we introduce, we develop an efficient column-generation algorithm for finding an optimal distribution for the team. We evaluate it on a suite of common benchmark games. It is three orders of magnitude faster than the prior state of the art on games that the latter can solve and it can also solve several games that were previously unsolvable.

Interpreting the Scope of AI job Market in the US in current times.


We are living in a time where everything is digital. Disruptive technologies like artificial intelligence (AI) has become central to this transformation. From retail to Fintech and cybersecurity to predictive analytics, tech pundits avow that AI now plays an essential cog in the future of these industries and disciplines. However, through some alarmists argue that AI is stealing jobs through automation and robotics, on the contrary, it has been observed that AI is also adding new job roles every day to the existing employment pool. Researchers have tracked down new job roles, occupations and emerging industries, in the AI landscape that can help us understand the job market better.

RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning Machine Learning

Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies that mostly explore for solving larger scale problems or accelerating solution efficiency, we propose a framework, RLCFR, which aims at improving the generalization ability of the CFR method. In the RLCFR, the game strategy is solved by the CFR in a reinforcement learning framework. And the dynamic procedure of iterative interactive strategy updating is modeled as a Markov decision process (MDP). Our method, RLCFR, then learns a policy to select the appropriate way of regret updating in the process of iteration. In addition, a stepwise reward function is formulated to learn the action policy, which is proportional to how well the iteration strategy is at each step. Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.

The Changing Face of AI Research


However, it is by no means clear yet whether this will project as a game-changer in the world ahead. Computer programmers have been trying hard to find the right and relevant pattern in data just to be sure they become extremely good at beating multiplayer games. A whitepaper published by researchers at Facebook and Carnegie Mellon University said their software is good at embracing randomness and that it is reliable to beat humans at games. Artificial intelligence is heralded as a solution to the complex problems faced by many industries and organizations. The prime concern for businesses today is to find out how to gain better insights into harnessing big data.

These online courses teach you how to win at online poker


TL;DR: The Ultimate Poker Pro Blueprint Mastery Bundle is on sale for £16.08 as of August 14, saving you 99% on list price. Playing poker online is a totally different game than playing in real life. You aren't playing other people so much as you are just playing the algorithm. Therefore, it requires a touch less skill and a touch more pattern recognition and smarts. In the Ultimate Poker Pro Blueprint Mastery Bundle, you'll learn exactly what it takes to win money playing poker online.

The Deck Is Not Rigged: Poker and the Limits of AI


Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player--or much of a poker fan, in fact--but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely--a view shared years later by Sandholm in his research with artificial intelligence. "Poker is the main benchmark and challenge program for games of imperfect information," Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.