This paper considers a capability construction problem of the C4ISR system under serviceoriented architecture. A capability construction model is first established and described in the planning domain definition language as an artificial intelligence (AI) planning problem. To adapt the complex requirements of a C4ISR system and large scale of required services, an incremental macro-operation learning method based on n-gram analysis is proposed, and an enhanced domain is generated using a relaxation scheme. To improve the efficiency of the search algorithm, an ordered-hill-climbing (OHC) method is designed based on the length of the operations. With the above procedures, the AI planner, using macro-operation and the OHC, is presented for capability construction problems. The simulation results show that this method can effectively shorten the search time of capability construction. INDEX TERMS Artificial intelligence planning, C4ISR, capability construction, service-oriented architecture.

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The dynamic resource scheduling problem is a field of intense research in command and control organization mission planning. This paper analyzes the emergencies in the battlefield first and divides them into three categories: the changing of task attributes, reduction of available platforms, and change in the number of tasks. To deal with these emergencies, in this paper, we built a series of multiobjective optimization models that maximizes the task execution quality and minimizes the cost of plan adjustment. To solve the model, we proposed an improved multi-objective evolutionary algorithm. A type of mapping operator and an improved crowding-distance sorting method are designed for the algorithm. Finally, the availability of the model and the solving algorithm were proved through a series of experiments. The Pareto frontier for the multi-objective dynamic resource scheduling problem can be found effectively, and the algorithm proposed in this paper shows better convergence compared with the AMP-NSGA-II algorithm.INDEX TERMS Command and control organization, dynamic resource scheduling, multi-objective evolutionary algorithm, multi-objective optimization.

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In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.