Knowledge graph embedding (KGE) has caught significant interest for its
effectiveness in knowledge graph completion (KGC), specifically link prediction
(LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly
growing literature, insufficient attention has been paid to the cooperation
between humans and AI on KG. However, humans' capability to analyze graphs
conceptually may further improve the efficacy of KGE models with semantic
information. To this effect, we carefully designed a human-AI team (HAIT)
system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging
fully human-designed ad-hoc dynamic programming (DP) on KG to produce human
insightful feature (HIF) vectors that capture the subgraph structural feature
and semantic similarities. By integrating HIF vectors into the training of KGE
models, notable improvements are observed across various benchmarks and
metrics, accompanied by accelerated model convergence. Our results underscore
the effectiveness of human-designed DP in the task of LP, emphasizing the
pivotal role of collaboration between humans and AI on KG. We open avenues for
further exploration and innovation through KG-HAIT, paving the way towards more
effective and insightful KG analysis techniques.

通过人工智能与人类的合作，我们设计了一个名为 KG-HAIT 的系统，利用人类对知识图谱的洞察力来改进知识图谱嵌入模型，通过动态规划生成捕捉子图结构特征和语义相似性的人类洞察特征向量，并将其整合到知识图谱嵌入模型的训练中，取得了显著的性能提升和模型收敛加速，强调了人工智能与人类在知识图谱上的合作的重要性。

人类洞察与人工智能精确度的协调：共同推进知识图任务

Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing  Knowledge Graph Task

Preference-based Reinforcement Learning (PbRL) is an active area of research,
and has made significant strides in single-agent actor and in observer
human-in-the-loop scenarios. However, its application within the co-operative
multi-agent RL frameworks, where humans actively participate and express
preferences for agent behavior, remains largely uncharted. We consider a
two-agent (Human-AI) cooperative setup where both the agents are rewarded
according to human's reward function for the team. However, the agent does not
have access to it, and instead, utilizes preference-based queries to elicit its
objectives and human's preferences for the robot in the human-robot team. We
introduce the notion of Human-Flexibility, i.e. whether the human partner is
amenable to multiple team strategies, with a special case being Specified
Orchestration where the human has a single team policy in mind (most
constrained case). We propose a suite of domains to study PbRL for Human-AI
cooperative setup which explicitly require forced cooperation. Adapting
state-of-the-art single-agent PbRL algorithms to our two-agent setting, we
conduct a comprehensive benchmarking study across our domain suite. Our
findings highlight the challenges associated with high degree of
Human-Flexibility and the limited access to the human's envisioned policy in
PbRL for Human-AI cooperation. Notably, we observe that PbRL algorithms exhibit
effective performance exclusively in the case of Specified Orchestration which
can be seen as an upper bound PbRL performance for future research.

人机协作中基于偏好的强化学习 (PbRL) 是一个活跃的研究领域，并在单个代理人和观察者在环路场景中取得了显著的进展。然而，在人机合作的多代理强化学习框架中，在人类积极参与并表达对代理行为偏好的应用仍然是未知的。