部分可观测下基于时序知识图谱记忆的神 经-符号元策略研究
Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
AI 导读
一项被 ISWC 2026 接收的研究提出一种神经-符号元策略，在每个决策点学习应采用哪种符号记忆启发式，同时保持符号化执行。该设置在 RoomKG 环境中用 RDF 图表示隐藏状态与观测，记忆通过时序 RDF 三元组标注扩展。
正文 · 原文
Abstract:Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic meta-policy that learns which symbolic memory heuristic to apply at each decision point while keeping execution symbolic. Our setting uses temporal knowledge-graph memory in RoomKG, where hidden state and observations are represented as Resource Description Framework (RDF) graphs and memory is augmented with temporal RDF triple annotations. The model combines knowledge-graph encoding of memory contents with value heads for question answering, exploration, and forgetting, yielding a controller that is both adaptive and inspectable. This gives the work a direct Semantic Web grounding through RDF-based representation, annotation-compatible graph semantics, and graph-based symbolic operations over explicit memory state. On train/test room splits at long-term memory capacity of 512, the qualifier-aware StarE-GNN configuration achieves the best held-out performance among the compared symbolic, neural, and neuro-symbolic systems while preserving step-level traceability of memory-management decisions.
| Comments: | Accepted at ISWC 2026 (Research Track), LNCS 17126, pp. 191-209. v5: accepted-manuscript notice on the first page, wider margins |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.18368 [cs.AI] |
| (or arXiv:2607.18368v5 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18368 arXiv-issued DOI via DataCite |
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| Journal reference: | ISWC 2026, LNCS 17126, pp. 191-209 |
| Related DOI: | https://doi.org/10.1007/978-3-032-42015-2_10
DOI(s) linking to related resources |
Submission history
From: Taewoon Kim [view email]
[v1]
Mon, 20 Jul 2026 17:55:00 UTC (210 KB)
[v2]
Fri, 24 Jul 2026 20:23:23 UTC (194 KB)
[v3]
Tue, 28 Jul 2026 13:21:18 UTC (200 KB)
[v4]
Sat, 3 Oct 2026 07:26:30 UTC (194 KB)
[v5]
Thu, 29 Oct 2026 16:35:11 UTC (193 KB)
来源：arXiv cs.AI · arxiv.org