Let’s look at how RL agents are trained to deal with ambiguity, and it may provide a blueprint of leadership lessons to ...

Abstract: Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Multi-AI agents – multiple artificial intelligence agents working together in a shared environment – can be used to address persistent workflow challenges in clinical decision support, drawing from ...

Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be.

With Visual Studio Code 1.107, developers can use GitHub Copilot and custom agents together and delegate work across local, background, and cloud agents. Just-released Visual Studio Code 1.107, the ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Abstract: Multi-task multi-agent reinforcement learning (M T-MARL) has recently gained attention for its potential to enhance MARL's adaptability across multiple tasks. However, it is challenging for ...

Researchers at Meta, the University of Chicago, and UC Berkeley have developed a new framework that addresses the high costs, infrastructure complexity, and unreliable feedback associated with using ...

Cursor has for the first time introduced what it claims is a competitive coding model, alongside the 2.0 version of its integrated development environment (IDE) with a new feature that allows running ...