Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play.

Strikes on Qatar, Poland shake U.S. sway among friend and foe U.S. President Donald Trump speaks at the Museum of the Bible in Washington on Sept. 8. Israeli strikes in Qatar and Russian drone incursions into Poland have challenged the U.S. leaders claims of global respect. The shock Israeli strike against Hamas officials in Doha, followed by Russian drones piercing the airspace of NATO ally Poland, delivered twin blows to President Donald Trump's longtime boast that friends and foes respect the U.S. under his leadership like never before. Trump voiced unhappiness with Israeli Prime Minister Benjamin Netanyahu for sending warplanes to attack the capital of Qatar, a major U.S. ally, but has otherwise resisted getting more involved. He has declined to denounce Russia's drone incursion into Poland, saying Thursday it could have been a mistake but regardless I'm not happy about anything having to do with that whole situation." Trump's removed attitude contrasts sharply with his repeated claims about his unique ability to solve the world's intractable conflicts. While he has conceded that Russia's war in Ukraine -- which he once pledged to resolve on his first day back in office -- has been more difficult than anticipated, he recently said President Vladimir Putin wants to make a deal for me."

The paper builds on well-known methods (CFR) and provides novel improvements and modifications that extend the approach to a multiplayer, hidden-role setting. This is original and novel and creative, though the crucial role of CFR cannot be understated. Related work appears to be adequately cited. The empirical results provide the main validation for the soundness and quality of the proposed algorithm; this is reasonable and is explained well in the paper. I have not spotted any obvious illogicalities or mistakes.

All reviewers agree that the paper provides some nice contributions (extending CFR beyond 2 players and tackling Avalon) and that the authors succeed well with their rebuttal to address some of the major concerns brought on by some of the referees. They have responded adequately and furthermore open-sourced their implementation. We expect the authors though to carry out the promised changes (and also improve on the notation).

Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on "The Resistance: Avalon", the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play.

Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on "The Resistance: Avalon", the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play.

"Across the board, AI will make predictive analysis markedly easier," Coleman adds. "AI greatly enhances the ability to detect and identify threats. This helps security teams stay ahead of attacks. Experts agree that this new era in tech and cybersecurity is driven by prediction, detection, and response." Anthony Ferrante, senior managing director and global head of cybersecurity at FTI Consulting, says the use of AI and machine learning to work smarter, advance business objectives, and protect against cyber threats is becoming more and more prevalent.

The data scarcity of user preferences and the cold-start problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social friends and foes can efficiently face both problems. In this study, we propose a strategy that performs social deep pairwise learning. Firstly, we design a ranking loss function incorporating multiple ranking criteria based on the choice in users, and the choice in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinear correlations between user preferences and the social information of trust and distrust relationships via a deep learning strategy. In each backpropagation step, we follow a social negative sampling strategy to meet the multiple ranking criteria of our ranking loss function. We conduct comprehensive experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature. The experimental results demonstrate that the proposed model beats other state-of-the art methods, attaining an 11.49% average improvement over the most competitive model. We show that our deep learning strategy plays an important role in capturing the nonlinear correlations between user preferences and the social information of trust and distrust relationships, and demonstrate the importance of our social negative sampling strategy on the proposed model.

Artificial Intelligence and machine learning are increasingly promoted as a solution in the context, and you don't need to look far to find products and services trumpeting their use of such techniques as a key selling point. At the same time, there is a growing concern that the growth of AI will herald a new era of cybercrime, making attacks more complex and more difficult to stop. Before looking at the potential downsides, it is worth thinking about the defensive role the AI can play in cybersecurity. The key areas here will include spotting things that we would not ordinarily notice and aiding the automation of things that otherwise rely upon manual intervention. Although it's getting significant attention at present, the opportunity to link AI into cybersecurity has been recognised for quite some time, with application in behavioural profiling and anomaly detection (e.g.