海战场C4ISR系统的服务自动组合方法

针对C⁴ISR系统中服务人工智能(artificial intelligence， AI)规划组合存在的服务数量大、不能处理服务执行过程中动态产生的新个体、业务流程和服务质量不确定等问题，提出了一种基于离散事件演算和马尔可夫逻辑网(discrete event calculus Markov logic network， DEC-MLN)的服务自动组合方法。首先给出了面向服务的海战场C⁴ISR系统的流程图，用对事件演算进行拓展形成的DEC-MLN对服务的输入、输出、前提条件和执行效果及基本服务流程进行建模，用MLN的权重刻画组合过程中业务流程和服务质量的不确定性。接着提出了一种基于模板的服务自动组合框架，并给出了溯因DEC规划方法。所提方法克服了基于传统AI规划组合方法限于顺序组合的问题；且DEC-MLN的谓词规模较小，提高了服务组合效率；DEC-MLN在处理不确定性的基础上又能解除封闭世界假说的限制，可以处理组合执行过程中新增个体，增加了组合的动态性。最后用实例验证了所提方法的可行性与有效性。

Service automatic composition method of C4ISR system on naval fields

A service automatic composition method based on discrete event calculus-Markov logic networks(DEC-MLN) is proposed aiming at the issues of service AI planning composition method such as a large number of services, incapability of handling dynamically created individuals during services’ execution and the uncertainty of business process and QoS in C⁴ISR systems. Firstly, the process chart of theC⁴ISR system on the naval fields is given and a probability extension of EC using MLN is presented. Then the basic semantic Web service composition processes and their IOPE are modeled based on the DEC MLN. The uncertainty of the composition business process and QoS is described by the weight of MLN. The service automatic composition framework based on template is introduced and the abduction DEC planning method is given. The proposed method solves the confine of sequence composition process of the classic AI planning composition method, and the predicate number of the DEC-MLN is smaller, which speeds the composition. The DEC-MLN can handle the uncertainty and dynamically create individuals during services’ execution well, and it can also rule out the bound of the closed-world assumption. Finally, the experiment shows the feasibility and efficiency of the proposed method.