Competitive influence maximization has been studied for several years, and various frameworks have been proposed to model different aspects of information diffusion under the competitive environment. This work presents a new gameboard for two competing parties with some new features representing loyalty in social networks and reflecting the attitude of not completely being loyal to a party when the opponent offers better suggestions. This behavior can be observed in most political occasions where each party tries to attract people by making better suggestions than the opponent and even seeks to impress the fans of the opposition party to change their minds. In order to identify the best move in each step of the game framework, an improved Monte Carlo tree search is developed, which uses some predefined heuristics to apply them on the simulation step of the algorithm and takes advantage of them to search among child nodes of the current state and pick the best one using an epsilon-greedy way instead of choosing them at random. Experimental results on synthetic and real datasets indicate the outperforming of the proposed strategy against some well-known and benchmark strategies like general MCTS, minimax algorithm with alpha-beta pruning, random nodes, nodes with maximum threshold and nodes with minimum threshold.

Lauren Rhue researches the fast-paced world of artificial intelligence and machine learning technology. But she wants everyone in it to slow down. Rhue, an assistant professor of information systems at the University of Maryland Robert H. Smith School of Business, recently audited emotion recognition technology within three facial recognition services: Amazon Rekognition, Face and Microsoft. Her research revealed what Rhue called "really stark" racial disparities. Amazon Rekognition is offered for use to other companies.

AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games. Here we are interested in gaining new insights into the ancient game of Go. For that purpose, we need to define new numerical measures based on the raw output of the engines. In this paper, we develop a numerical tool for automated move-by-move performance evaluation in a context-sensitive manner and for recognizing game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. Here we investigate the properties of this measure and describe some applications.

The NFL and lawyers for thousands of retired NFL players have reached an agreement to end race-based adjustments in dementia testing in the $1 billion settlement of concussion claims, according to a proposed deal filed Wednesday in federal court. The revised testing plan follows public outrage over the use of "race-norming," a practice that came to light only after two former NFL players filed a civil rights lawsuit over it last year. The adjustments, critics say, may have prevented hundreds of Black players suffering from dementia to win awards that average $500,000 or more. The Black retirees will now have the chance to have their tests rescored or, in some cases, seek a new round of cognitive testing, according to the settlement, details of which were first reported in The New York Times on Wednesday. "We look forward to the court's prompt approval of the agreement, which provides for a race-neutral evaluation process that will ensure diagnostic accuracy and fairness in the concussion settlement," NFL lawyer Brad Karp said in a statement. The proposal, which must still be approved by a judge, follows months of closed-door negotiations between the NFL, class counsel for the retired players, and lawyers for the Black players who filed suit, Najeh Davenport and Kevin Henry.

There is "evident bias" in some football commentary relating to the skin tone of players, according to a new study. In 80 televised games analysed across four European leagues, including the Premier League, players with a lighter skin tone were praised more often for their intelligence and work ethic. Meanwhile, those with darker skin tones were "significantly" more likely to be "reduced to their physical characteristics or athletic ability", such as their pace and power. The research, conducted by Danish firm RunRepeat in association with the Professional Footballers' Association (PFA), concluded that the findings showed "bias from commentators". "The continuous praise for players with lighter skin tone for their skill level, leadership and cognitive abilities combined with the continuous criticism for players with darker skin tone is likely to influence the perception of the soccer watching public," said the researchers.

We investigate systematically the impact of human intervention in the training of computer players in a strategy board game. In that game, computer players utilise reinforcement learning with neural networks for evolving th eir playing strategies and demonstrate a slow learning speed. Human intervention can significan tly enhance learning performance, but carrying it out systematically seems to be more of a problem of an integrated game development environment as opposed to automatic evolutionary learning.

In this paper we experiment with a 2-player strategy board game where playing models are evolved using reinforcement learning and neural networks. The models are evolved to speed up automatic game development based on human involvement at varying levels of sophistication and density when compared to fully autonomous playing. The experimental results suggest a clear and measurable association between the ability to win games and the ability to do that fast, while at the same time demonstrating that there is a minimum level of human involvement beyond which no learning really occurs.