Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios
Smart manufacturing is arriving. It promises a future of mass-producing highly personalized
products via responsive autonomous manufacturing operations at a competitive cost. Of …
products via responsive autonomous manufacturing operations at a competitive cost. Of …
An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arxiv preprint arxiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
[BUCH][B] Grundkurs künstliche intelligenz
W Ertel, NT Black - 2016 - Springer
Der Begriff Künstliche Intelligenz weckt Emotionen. Zum einen ist da die Faszination der
Intelligenz, die offenbar uns Menschen eine besondere Stellung unter den Lebewesen …
Intelligenz, die offenbar uns Menschen eine besondere Stellung unter den Lebewesen …
Camel: Communicative agents for" mind" exploration of large language model society
The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …
complex task-solving. However, their success heavily relies on human input to guide the …
On generative agents in recommendation
Recommender systems are the cornerstone of today's information dissemination, yet a
disconnect between offline metrics and online performance greatly hinders their …
disconnect between offline metrics and online performance greatly hinders their …
The surprising effectiveness of ppo in cooperative multi-agent games
Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …
Multi-agent deep reinforcement learning: a survey
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Tptu: Task planning and tool usage of large language model-based ai agents
With recent advancements in natural language processing, Large Language Models (LLMs)
have emerged as powerful tools for various real-world applications. Despite their prowess …
have emerged as powerful tools for various real-world applications. Despite their prowess …
Artificial intelligence and management: The automation–augmentation paradox
S Raisch, S Krakowski - Academy of management review, 2021 - journals.aom.org
Taking three recent business books on artificial intelligence (AI) as a starting point, we
explore the automation and augmentation concepts in the management domain. Whereas …
explore the automation and augmentation concepts in the management domain. Whereas …
Is independent learning all you need in the starcraft multi-agent challenge?
Most recently developed approaches to cooperative multi-agent reinforcement learning in
the\emph {centralized training with decentralized execution} setting involve estimating a …
the\emph {centralized training with decentralized execution} setting involve estimating a …