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 …
H Mossalam, YM Assael, DM Roijers… - arXiv preprint arXiv …, 2016 - arxiv.org
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 …
T Hu, B Luo, C Yang, T Huang - IEEE Transactions on Pattern …, 2023 - ieeexplore.ieee.org
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 …