In 1971, three student teachers at Carleton College in Minnesota created a computer game to teach their students about the westward expansion. Don Rawitsch, Bill Heinemann, and Paul Dillenberger programmed the game in BASIC language on an HP 2100 minicomputer with only 32 kilobytes of memory. The game was designed to simulate the experience of a family traveling from Missouri to Oregon in 1848 and teach students about the challenges faced by pioneers on the Oregon Trail. The game became popular in classrooms across the United States and eventually was published by MECC (Minnesota Educational Computing Consortium) in 1985. Over the years, "The Oregon Trail" has undergone numerous updates and re-releases for various platforms. The original version of the game was text-based, and players had to use the arrow keys to navigate their wagon.

Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders and successfully won the majority vote. By optimising for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation.

Welcome to Pushing Buttons, the Guardian's gaming newsletter. If you'd like to receive it in your inbox every week, just pop your email in below – and check your inbox (and spam) for the confirmation email. This week marks a truly important video game anniversary: it is 50 years since Nolan Bushnell and Ted Dabney incorporated Atari Inc, the company that laid the foundations for the video games industry. There have been many appraisals of the company and its landmark achievements in the games press over the past few days – from the arrival of a Pong machine in Andy Capp's Tavern in Sunnyvale, California, in 1972, through classic titles such as Breakout, Asteroids and Missile Commands, to the iconic home consoles. But one element that often gets overlooked in these nostalgic reveries is the way in which Atari taught the first generation of electronic gamers how to think symbolically.

The Backpropagation algorithm is the powerhouse of all Deep Learning models. It is one of the methods of efficiently calculating Gradient for billions of parameters. Therefore, it is vital to understand how the Gradient information guides the function's parameters to traverse the loss-landscape and eventually halt in a valley. I thought of building a simple game centred around the concepts to ensure whether a learner has understood them or not. Here it is for you to explore. The video below is my attempt to reach the minimum of the function.

Weighted voting games is one of the most important classes of cooperative games. Recently, Zhang and Grossi [53] proposed a variant of this class, called delegative simple games, which is well suited to analyse the relative importance of each voter in liquid democracy elections. Moreover, they defined a power index, called the delagative Banzhaf index to compute the importance of each agent (i.e., both voters and delegators) in a delegation graph based on two key parameters: the total voting weight she has accumulated and the structure of supports she receives from her delegators. We obtain several results related to delegative simple games. We first propose a pseudo-polynomial time algorithm to compute the delegative Banzhaf and Shapley-Shubik values in delegative simple games. We then investigate a bribery problem where the goal is to maximize/minimize the voting power/weight of a given voter in a delegation graph by changing at most a fixed number of delegations. We show that the problems of minimizing/maximizing a voter's power index value are strongly NP-hard. Furthermore, we prove that having a better approximation guarantee than $1-1/e$ to maximize the voting weight of a voter is not possible, unless $P = NP$, then we provide some parameterized complexity results for this problem. Finally, we show that finding a delegation graph with a given number of gurus that maximizes the minimum power index value an agent can have is a computationally hard problem.

Artificial Intelligence for Simple Games - Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games! Created by Jan Warchocki, Ligency TeamPreview this Course - GET COUPON CODE Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming? If you're looking for a creative way to dive into Artificial Intelligence, then'Artificial Intelligence for Simple Games' is your key to building lasting knowledge. Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more. Whether you're an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.

The paper develops a theory of power for delegable proxy voting systems. We define a power index able to measure the influence of both voters and delegators. Using this index, which we characterize axiomatically, we extend an earlier game-theoretic model by incorporating power-seeking behavior by agents. We analytically study the existence of pure strategy Nash equilibria in such a model. Finally, by means of simulations, we study the effect of relevant parameters on the emergence of power inequalities in the model.

Cooperative games provide a framework to study cooperation among self-interested agents. They offer a number of solution concepts describing how the outcome of the cooperation should be shared among the players. Unfortunately, computational problems associated with many of these solution concepts tend to be intractable---NP-hard or worse. In this paper, we incorporate complexity measures recently proposed by Feige and Izsak (2013), called dependency degree and supermodular degree, into the complexity analysis of coopera- tive games. We show that many computational problems for cooperative games become tractable for games whose dependency degree or supermodular degree are bounded. In particular, we prove that simple games admit efficient algorithms for various solution concepts when the supermodular degree is small; further, we show that computing the Shapley value is always in FPT with respect to the dependency degree. Finally, we observe that, while determining the dependency among players is computationally hard, there are efficient algorithms for special classes of games.

This post is about implementing a – quite basic – Neural Network that is able to play the game Tic-Tac-Toe. For sure there is not really a need for any Neural Network or Machine Learning model to implement a good – well, basically perfect – computer player for this game. This could be easily achieved by using a brute-force approach. But as this is the author's first excursion into the world of Machine Learning, opting for something simple seems to be a good idea. The motivation to start working on this post and the related project can be comprised in one word: AlphaGo. The game of Go is definitely the queen of competitive games.

The notion of machines rising up against their creators is a common theme in culture and in breathless news coverage. That helps explain the lurid headlines in recent days describing how Facebook AI researchers in a "panic" were "forced" to "kill" their "creepy" bots that had started speaking in their own language. That's not quite what happened. A Facebook experiment did produce simple bots that chattered in garbled sentences, but they weren't alarming, surprising, or very intelligent. Nobody at the social network's AI lab panicked, and you shouldn't either.