Autonomous agents acting in the real-world often operate based on models that ignore
certain aspects of the environment. The incompleteness of any given model–handcrafted or …

Autonomous systems that operate in the open world often use incomplete models of their
environment. Model incompleteness is inevitable due to the practical limitations in precise …

Autonomous systems are often deployed in the open world where it is hard to obtain
complete specifications of objectives and constraints. Operating based on an incomplete …

Designing reward functions for reinforcement learning is difficult: besides specifying which
behavior is rewarded for a task, the reward also has to discourage undesired outcomes …

Deployed algorithms can cause certain negative side effects on the world in pursuit of their
objective. It is important to define precisely what an algorithmic side-effect is in a way which …

When agents that are independently trained (or designed) to complete their individual tasks
are deployed in a shared environment, their joint actions may produce negative side effects …

The capabilities of deep learning systems have advanced much faster than our ability to
understand them. Whilst the gains from deep neural networks (DNNs) are significant, they …

Autonomous systems perform various tasks across different industries ranging from finance
to healthcare to space applications. However, these systems are often deployed in the open …

The rapid growth in the deployment of autonomous systems across various sectors has
generated considerable interest in how these systems can operate reliably in large …

The advancement of AI has enabled high-level automation in complex environments.
However, autonomous systems in the real-world may act in an unsafe manner when there …