A robust body of reinforcement learning techniques have been developed to solve complex
sequential decision making problems. However, these methods assume that train and …

Algorithms for automated novelty detection and management are of growing interest but
must address the inherent uncertainty from variations in non-novel environments while …

Open-world novelty--a sudden change in the mechanics or properties of an environment--is
a common occurrence in the real world. Novelty adaptation is an agent's ability to improve its …

This survey comprehensively reviews the multi-dimensionality of game scenario diversity,
spotlighting the innovative use of procedural content generation and other fields as …

Due to the emergence of AI systems that interact with the physical environment, there is an
increased interest in incorporating physical reasoning capabilities into those AI systems. But …

Due to the emergence of AI systems that interact with the physical environment, there is an
increased interest in incorporating physical reasoning capabilities into those AI systems. But …

The ability of AI agents and architectures to detect and adapt to sudden changes in their
environments remains an outstanding challenge. In the context of multi-agent games, the …

Learning to detect, characterize and accommodate novelties is a challenge that agents
operating in open-world domains need to address to be able to guarantee satisfactory task …

Contemporary artificial intelligence (AI) systems have excelled in well-defined tasks within
controlled environments, often surpassing human performance, especially in complex …

Current artificial intelligence (AI) systems are proficient at tasks in a closed-world setting
where the rules are often rigid. However, in real-world applications, the environment is …