arXiv paper search: top 5 papers with authors, date, link and abstract. Input: s...

{ "body": { "input": "agent payments" }, "bodyType": "json", "method": "POST", "type": "http" }

{ "example": { "papers": [ { "authors": [ "Changxi Zhu", "Mehdi Dastani", "Shihan Wang" ], "published": "2022-03-16", "summary": "Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agen", "title": "A Survey of Multi-Agent Deep Reinforcement Learning with Communication", "url": "http://arxiv.org/abs/2203.08975v2" }, { "authors": [ "Bartosz Bartkowski", "Nils Droste", "Mareike Ließ" ], "published": "2019-08-22", "summary": "From a theoretical point of view, result-based agri-environmental payments are clearly preferable to action-based payments. However, they suffer from two major practical disadvantages: costs of measuring the results and payment uncertainty ", "title": "Implementing result-based agri-environmental payments by means of modelling", "url": "http://arxiv.org/abs/1908.08219v2" }, { "authors": [ "Taiyu Zhang", "Xuesong Zhang", "Robbe Cools" ], "published": "2024-09-03", "summary": "In the domain of Human-Computer Interaction, focus groups represent a widely utilised yet resource-intensive methodology, often demanding the expertise of skilled moderators and meticulous preparatory efforts. This study introduces the ``Fo", "title": "Focus Agent: LLM-Powered Virtual Focus Group", "url": "http://arxiv.org/abs/2409.01907v1" } ], "query": "agent payments", "source": "arXiv API" }, "type": "json" }