任务决策是实现无人艇智能自主的关键环节，其存在着场景多样性、状态不确定性、约束动态性等问题。为此，本文提出动态多实体贝叶斯网络模型。首先设计了面向无人艇的语义推理框架，对无人艇本体信息进行知识表示。进一步，采用概率本体语言方法描述不确定性知识，同时扩展动态贝叶斯网络的结构，从而提高了无人艇任务决策在不确定性因素和时序因素影响下的推理能力。最后针对岛礁区域防守的任务场景，与多实体贝叶斯网络模型进行对比，结果表明DMEBN模型在动态条件下具有执行策略的连续性，验证了模型的可行性和有效性。
Mission decision-making is the key component to realize the intelligent autonomy of unmanned surface vehicles, which has the problems of scenario diversity, state uncertainty, and constraint dynamics. To solve the above limitations, the dynamic multi-entity Bayesian network (DMEBN) model is proposed. In order to perform knowledge representation of unmanned surface vehicle ontology information, a semantic reasoning framework for unmanned surface vehicles is designed. The probabilistic ontology language is used to describe uncertainty knowledge while extending the structure of dynamic Bayesian networks, thus enhancing the reasoning ability of unmanned surface vehicle decision-making under the influence of uncertainty factors and temporal sequence. The model is applied to the mission scenario of island area defense and compared with the model of multi-entity Bayesian network to show the continuity of the DMEBN model in executing strategies under dynamic conditions and to verify the feasibility and validity of the model.
2023,45(8): 78-83 收稿日期：2022-06-27
DOI：10.3404/j.issn.1672-7649.2023.08.016
分类号：TP181
基金项目：国家自然科学基金资助项目(61991412)
作者简介：刘力源(1998-),男,硕士研究生,研究方向为任务决策、知识推理
