Understanding how logical agents cooperate or fight, especially in the face of resource scarcity, is a fundamental problem for social scientists. But soon, this problem will also be at the heart of how we understand, control, and cooperate with artificially intelligent agents, and how they work among themselves. Researchers inside of Google's AI DeepMind project wanted to know whether distinct artificial intelligence agents worked together or competed when faced with a problem. Doing this experiment would help scientists understand how our future networks of smart systems may work together. The researchers pitted two AIs against each other in a couple of video games.

Google's artificial intelligence subsidiary DeepMind is pitting AI agents against one another to test how they interact with each other and how they would react in various "social dilemmas". In a new study, researchers said they used two video games – Wolfpack and Gathering – to examine how AI agents change the way they behave based on the environment and situation they are in using social sciences and game theory principles. "The question of how and under what circumstances selfish agents cooperate is one of the fundamental questions in the social sciences," DeepMind researchers wrote in a blog post. "One of the simplest and most elegant models to describe this phenomenon is the well-known game of Prisoner's Dilemma from game theory." This well-known principle is based on the scenario where two arrested suspects jointly accused of a crime are questioned separately.

Bostjan Kaluza, PhD, is a researcher in artificial intelligence and machine learning. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insight. Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Bostjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications.

Artificial intelligence (AI) agents have the potential to become aggressive or work in teams, according to researchers at DeepMind. A paper released by five computer scientists from the London-based company, which is owned by Google, used games to look at how AIs behave alongside one another. Joel Leibo, a research scientist at DeepMind and the lead author on the paper, told Business Insider on Thursday: "We were interested in the factors affecting cooperation." When asked about AI aggression, Leibo stressed: "We have to be careful not to anthropomorphise too much. These are toy problems aimed at exploring cooperative versus competitive dynamics." Describing the study in a blog post on the DeepMind website, the researchers said that they used two basic video games called "Wolfpack" and "Gathering" to analyse the behaviour of AI agents.

Artificial intelligence is slowly creeping into our daily lives, and soon, it will become necessary to understand how different agents behave in social dilemmas. To see what would happen in such a scenario, Google's DeepMind researchers developed two games known as'Gathering' and'Wolfpack,' which build off the Prisoner's Dilemma from game theory. Over time, the AI agents learned how to behave rationally – and while they showed the researchers that they would sometimes cooperate, the games revealed the AI would turn on others when necessary. In the first game, known as Gathering, the AI agents (Red and Blue) are tasked with collecting apples. They can also'tag' the other player by shooting a beam at them, which would remove temporarily remove the tagged agent from the game.

Artificial intelligence changes the way it behaves based on the environment it is in, much like humans do, according to the latest research from DeepMind . Computer scientists from the Google-owned firm have studied how their AI behaves in social situations by using principles from game theory and social sciences. During the work, they found it is possible for AI to act in an "aggressive manner" when it feels it is going to lose out, but agents will work as a team when there is more to be gained. For the research, the AI was tested on two games: a fruit gathering game and a Wolfpack hunting game. These are both basic, 2D games that used AI characters (known as agents) similar to those used in DeepMind's original work with Atari.

In the future, it's likely that many aspects of human society will be controlled -- either partly or wholly -- by artificial intelligence. AI computer agents could manage systems from the quotidian (e.g., traffic lights) to the complex (e.g., a nation's whole economy), but leaving aside the problem of whether or not they can do their jobs well, there is another challenge: will these agents be able to play nice with one another? What happens if one AI's aims conflict with another's? Will they fight, or work together? Google's AI subsidiary DeepMind has been exploring this problem in a new study published today.

Artificial intelligence changes the way it behaves based on the environment it is in, much like humans do, according to the latest research from DeepMind . Computer scientists from the Google-owned firm have studied how their AI behaves in social situations by using principles from game theory and social sciences. During the work, they found it is possible for AI to act in an "aggressive manner" when it feels it is going to lose out, but agents will work as a team when there is more to be gained. For the research, the AI was tested on two games: a fruit gathering game and a Wolfpack hunting game. These are both basic, 2D games that used AI characters (known as agents) similar to those used in DeepMind's original work with Atari.

Data evaluation is crucial for gaining information from sensor networks. Main challenges include processing speed and adaptivity to system change, both prerequisites for SHM-based weight reduction via relaxed safety factors. Our study looks at soft real time solutions providing feedback within defined but flexible, application-controlled intervals. These can rely on minimizing computation/communication latencies e.g. by parallel computation. Strategies towards this aim can be model-based, including inverse FEM, or model-free, including machine learning, which in practice bases training on a defined system state, too, hence also facing challenges at state changes.

The application of swarm intelligence (SI) in the optimization field has been gaining much popularity, and various SI algorithms have been proposed in last decade. However, with the increased number of SI algorithms, most research focuses on the implementation of a specific choice of SI algorithms, and there has been rare research analyzing the common features among SI algorithms coherently. More importantly, no general principles for the implementation and improvement of SI algorithms exist for solving various optimization problems. In this research, aiming to cover such a research gap, a unified framework towards SI is proposed inspired by the in-depth analysis of SI algorithms. The unified framework consists of the most frequently used operations and strategies derived from typical examples of SI algorithms.