本体介导的多利益相关方神经符号约束获取
Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders
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中文总结 AI 辅助
本文提出一种本体介导的神经符号架构，利用OWL本体统一LLM软偏好与硬件硬约束，通过描述逻辑检测冲突并生成解释以交互式重协商，适用于多利益相关方约束获取场景。
中文摘要 AI 辅助
神经符号研究通常假设存在预先存在的符号规范，而将需求和约束的获取与形式化这一上游挑战在很大程度上未得到解决。我们提出了一种架构，通过使用OWL配置本体在神经约束源和下游消费者之间进行中介，填补了这一空白。在该框架中，LLM助手引出软性的利益相关方偏好，而硬件规范则定义硬性的物理和工程限制。本体统一这些异构输入，利用描述逻辑识别不可满足性，并生成符号解释，使LLM能够与用户交互式地重新协商条款。任何剩余的冲突通过基于优先级的松弛在下游解决。我们在FLEXI项目的微电网用例中展示了我们的方法，并论证了其在多利益相关方领域的通用性，在这些领域中，约束获取分布在人类和自动化来源之间，且权力不平等。
英文摘要
Neurosymbolic research typically assumes a pre-existing symbolic specification, leaving the upstream challenge of acquiring and formalizing requirements and constraints largely unaddressed. We present an architecture that fills this gap by using an OWL configuration ontology to mediate between neural constraint sources and downstream consumers. In this framework, LLM assistants elicit soft stakeholder preferences, while hardware specifications define hard physical and engineering limits. The ontology unifies these heterogeneous inputs, leverages description logic to identify unsatisfiability, and generates symbolic explanations that enable LLMs to interactively renegotiate terms with users. Any remaining conflicts are resolved downstream via priority-based relaxation. We illustrate our approach on a microgrid use case from the FLEXI project and argue its generalizability to multi-stakeholder domains where constraint acquisition is distributed across human and automated sources of unequal authority.
发表机构
- Siemens AG Österreich（西门子奥地利股份公司）
机构由 AI 辅助整理，请以论文原文为准。
