Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …

We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-
objective decision problems where the relative importances of the objectives are not known …

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to
constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of …

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL)
framework based on deep Q-networks. We develop a high-performance MODRL framework …

Deep reinforcement learning (RL) has been applied extensively to solve complex decision-
making problems. In many real-world scenarios, tasks often have several conflicting …

The research initiative “self-improving system integration”(SISSY) was established with the
goal to master the ever-changing demands of system organisation in the presence of …

This chapter discusses the role of self-awareness for adaptation at the individual level, when
one single entity receives inputs both from itself or some of its components and from the …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …