From the 1 of 5 linked papers with an AI index.
5 papers
DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Qintong Xie, Edward Koh, Xavier Cadet +1
The paper introduces DNQ, a deep reinforcement learning framework that trains bidding agents for partially observable n‑player games by alternating between trajectory collection, c…
Retrieval-Augmented LLMs for Security Incident Analysis
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authe…
AsymPuzl: An Asymmetric Puzzle for multi-agent cooperation
Xavier Cadet, Edward Koh, Peter Chin
Large Language Model (LLM) agents are increasingly studied in multi-turn, multi-agent scenarios, yet most existing setups emphasize open-ended role-play rather than controlled eval…
Quantitative Resilience Modeling for Autonomous Cyber Defense
Xavier Cadet, Simona Boboila, Edward Koh +2
Cyber resilience is the ability of a system to recover from an attack with minimal impact on system operations. However, characterizing a network's resilience under a cyber attack…
Nash Q-Network for Multi-Agent Cybersecurity Simulation
Qintong Xie, Edward Koh, Xavier Cadet +1
Cybersecurity defense involves interactions between adversarial parties (namely defenders and hackers), making multi-agent reinforcement learning (MARL) an ideal approach for model…