3 papers
cs.GT2026
Characterizing Robustness of Strategies to Novelty in Zero-Sum Open Worlds
Mayank Kejriwal, Shilpa Thomas, Hongyu Li
In open-world environments, artificial agents must often contend with novel conditions that deviate from their training or design assumptions. This paper studies the robustness of…
cs.AI2025
Generating Novelty in Open-World Multi-Agent Strategic Board Games
Mayank Kejriwal, Shilpa Thomas
We describe GNOME (Generating Novelty in Open-world Multi-agent Environments), an experimental platform that is designed to test the effectiveness of multi-agent AI systems when fa…
cs.LG2021
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
Trevor Bonjour, Marina Haliem, Aala Alsalem +5
Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires play…