Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior to catastrophic system failures. To formally define these effects, ...

This paper introduces a hierarchical approach utilizing nested dichotomies to enhance the MonuMAI framework designed for architectural image classification. The study focuses on developing a foundational layer dedicated to distinguishing between ...

Research on large language models (LLMs) is a prominent field in open-world machine learning. Despite their significant capabilities in natural language processing, LLMs face several challenges that must be overcome, namely, consistency, ...

Autonomous drones have been proposed for many industrial inspection roles including wind farms, railway lines and solar farms. They have many potential benefits, including accessing difficult to reach locations, reduced physical risk to operators ...

Large Language Models (LLMs) specialized in code have shown exceptional proficiency across various programming-related tasks, particularly code generation. Nonetheless, due to its nature of pretraining on massive uncritically filtered data, prior studies ...

The advent of advanced AI underscores the urgent need for comprehensive safety evaluations, necessitating collaboration across communities (i.e., AI, software engineering, and governance). However, divergent practices and terminologies across these ...

Recent developments in deep neural networks (DNNs) have led to their adoption in safety-critical systems, which in turn has heightened the need for guaranteeing their safety. These safety properties of DNNs can be proven using tools developed by ...

In this paper, we introduce new formal methods and provide empirical evidence to highlight a unique safety concern prevalent in reinforcement learning (RL)-based recommendation algorithms – ’user tampering.’ User tampering is a situation where an RL-...

Reinforcement Learning (RL), one of the most active research areas in artificial intelligence, focuses on goal-directed learning from interaction with an uncertain environment. RL systems play an increasingly important role in many aspects of ...

An important goal in reinforcement learning is to create agents that can quickly adapt to new goals but at the same time avoid situations that might cause damage to themselves or their environments. One way agents learn is through exploration mechanisms,...

Conditional Value-at-Risk (CVaR) is a well-known measure of risk that has been directly equated to robustness, an important component of Artificial Intelligence (AI) safety. In this paper we focus on optimizing CVaR in the context of Reinforcement ...