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Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams. Agents can be divided into types spanning simple to complex.
Artificial Intelligence for Environment & Sustainability (ARIES) is an international non-profit research project hosted by the Basque Centre for Climate Change (BC3) headquartered in Bilbao, Spain. [1] It was created to integrate scientific computational models for environmental sustainability assessment and policy-making, [2] [3] [4] through ...
In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. Leading AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal ...
Generative artificial intelligence (generative AI, GenAI, [166] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 167 ] [ 168 ] [ 169 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 170 ...
Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
"Autonomous agents are systems capable of autonomous, purposeful action in the real world." [2] According to Maes (1995): "Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." [3]
In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive.
The Riemann hypothesis catastrophe thought experiment provides one example of instrumental convergence. Marvin Minsky, the co-founder of MIT's AI laboratory, suggested that an artificial intelligence designed to solve the Riemann hypothesis might decide to take over all of Earth's resources to build supercomputers to help achieve its goal. [2]