发表机构
University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对辅助AI智能体的歧义问题，提出嵌入分层组合性原则的架构，结合语义兼容性等模型推理实现歧义消除，实验表明其性能优于当前最优数据驱动基线，可适配特定用户画像。
AI 中文摘要
人们正开发越来越多的AI智能体以在各类应用中辅助人类，大型语言模型及其他深度网络架构被视为这类智能体的当前最优技术。这些方法是出色的随机预测器，但存在资源消耗大、不透明的问题，且因底层表示与处理选择范围狭窄，在新情境下会做出任意决策。本研究旨在基于可追溯至早期AI先驱、但未被现代AI方法充分利用的核心原则，探索这类AI智能体的架构设计。本文围绕AI智能体解决人类参与者所指对象歧义这一核心问题展开研究：人类通过启发式方法，利用领域上下文的组合知识与其他人类参与者的偏好来解决此类歧义。受此观察启发，本文描述了一种嵌入分层组合性原则的架构，该架构使用简单启发式方法实现所需的歧义消除。具体而言，领域对象根据经人类验证的语义特征规范中的原始属性进行表示，这些属性与从辅助智能体和特定用户的有限交互历史中自动识别的概念进行分层组合。辅助智能体通过对该组合层次结构的知识、支配领域动态的公理、语义兼容性模型、会话显著性模型及用户特定主题偏好模型进行推理，在必要时请求人类澄清，从而实现所需的歧义消除。实验表明，本文方法始终优于当前最优的数据驱动基线，支持对特定用户画像的适配。
英文摘要
AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices. Our work seeks to explore the design of architectures for such AI agents based on core principles that can be traced back to the early pioneers of AI but are not fully utilized in modern AI methods. We do so in this paper in the context of the core problem of AI agents addressing ambiguity in the objects being referred to by the human participants. Humans address such ambiguity by heuristically leveraging compositional knowledge of domain context and the preferences of the other human participants. Drawing inspiration from this observation, we describe an architecture that embeds the principle of hierarchical compositionality and uses simple heuristics to achieve the desired disambiguation. Specifically, domain objects are represented in terms of primitive attributes drawn from human-validated semantic feature norms, and a hierarchical combination of attributes and concepts automatically identified from a limited observed history of interactions of an assistive agent with specific users. The assistive agent then achieves the desired disambiguation by reasoning with knowledge of this compositional hierarchy; axioms governing domain dynamics; and models of semantic compatibility, session salience, and user-specific thematic preference, requesting human clarification when necessary. Experiments show that our approach consistently outperforms state of the art data-driven baselines, supporting adaptation to specific user profiles.
Comments30 pages, 9 figures, 4 tables. Project page: https://tianyi-fu.github.io/HCAA
