PulseAugur
中
实时 22:17:47
English(EN) Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)

新模型探讨AI代理如何说服人类

作者 PulseAugur 编辑部 · [1 个来源] ·

一个名为“基于知识的说服模型”（KPM）的新理论框架被提出，用于理解生成式社交代理（GSAs）如何与人类用户互动并影响他们。该模型由Brandeis University的Stephan Vonschallen开发，他认为GSAs的自我知识、用户知识和情境知识决定了其说服策略，进而影响人类的态度和行为。该模型旨在指导开发负责任的GSAs，通过遵守社会规范和道德标准来促进用户福祉，并可能应用于医疗和教育等领域。 AI

影响 为开发更符合伦理且更有效的人机交互AI代理提供了框架。

排序理由 该集群包含一篇详细介绍新理论模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新模型探讨AI代理如何说服人类

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该集群包含一篇详细介绍新理论模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, safety
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
86 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Stephan Vonschallen, Friederike Eyssel, Theresa Schmiedel ·

    理解生成式社交代理与人类之间的说服性互动：基于知识的说服模型 (KPM)

    arXiv:2602.11483v2 Announce Type: replace-cross Abstract: Generative social agents (GSAs) use artificial intelligence to autonomously communicate with human users in a natural and adaptive manner. Currently, there is a lack of theorizing regarding interactions with GSAs, and like…

相关实体

相关话题