Agentic AI has emerged as the latest permutation of generative AI, enabling autonomous functionality as a way to deliver ...
The research-focused agent shows how a new generation of more capable AI models could automate some office tasks.
But few agree on what they actually do. In January, OpenAI released Operator, an agent that can browse the web, book travel ...
A single-task and multi-decision evolutionary game model based on multi-agent reinforcement learning
In the Markov decision framework, a single-task multi-decision evolutionary game model based on multi-agent reinforcement learning is proposed to explore the evolutionary rules in the process of a ...
Multi-Agent Hide and Seek in a 2D grid with DQN-based RL. Agents learn emergent strategies involving door toggling, locking, and hiding.
Research team developed CLAP, an RL agent for automated penetration testing. It features a coverage mechanism and Chebyshev ...
Introducing Dapr Agents—a groundbreaking framework for creating scalable AI agents using Large Language Models (LLMs). With ...
Microsoft announced Tuesday two significant additions to its Copilot Studio platform: deep reasoning capabilities that enable agents to tackle complex problems through careful, methodical thinking, ...
After Google and OpenAI offered up AI news on Tuesday, Microsoft has followed with announcements of its own, including ...
This thinking capability was first introduced by Google in its Gemini 2.0 Flash Thinking Experimental AI model, which was ...
Neurobehavioral data combined with computational models shows the superiority of active inference models in explaining human decisions under uncertainty.
Chatbots powered by large language models are still a nascent technology, and difficult to study. That’s why this kind of ...
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