Service-oriented air combat simulation architecture is proposed.The core design goal is high agility which represents the ability to accommodate the simulation requirements change.Its main idea is to design model unit...Service-oriented air combat simulation architecture is proposed.The core design goal is high agility which represents the ability to accommodate the simulation requirements change.Its main idea is to design model units as services that can communicate and interoperate with any other services at runtime.A service is autonomous and is fully defined by a description contract which contains some combination of syntactic,semantic,and behavioral information.Based on the architecture,air combat simulation system can be described as an abstract composition of description contracts.It becomes concrete at run time as services that implement the constituent description contracts are discovered and bind.The whole process is a continuous run-time activity that responds to simulation needs and the availability of services.This provides benefits of implementation transparency and minimal dependency between models.Thus,simulation system can minimize the impact of change on it and increase the overall efficiency to respond to requirements change.展开更多
Value function approximation plays an important role in reinforcement learning(RL)with continuous state space,which is widely used to build decision models in practice.Many traditional approaches require experienced d...Value function approximation plays an important role in reinforcement learning(RL)with continuous state space,which is widely used to build decision models in practice.Many traditional approaches require experienced designers to manually specify the formulization of the approximating function,leading to the rigid,non-adaptive representation of the value function.To address this problem,a novel Q-value function approximation method named‘Hierarchical fuzzy Adaptive Resonance Theory’(HiART)is proposed in this paper.HiART is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input automatically.HiART begins with a highly generalized structure where the number of the category nodes is limited,which is beneficial to speed up the learning process at the early stage.Then,the network is refined gradually by creating the attached subnetworks,and a layered network structure is formed during this process.Based on this adaptive structure,HiART alleviates the dependence on expert experience to design the network parameter.The effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation time.Finally,a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART.展开更多
文摘Service-oriented air combat simulation architecture is proposed.The core design goal is high agility which represents the ability to accommodate the simulation requirements change.Its main idea is to design model units as services that can communicate and interoperate with any other services at runtime.A service is autonomous and is fully defined by a description contract which contains some combination of syntactic,semantic,and behavioral information.Based on the architecture,air combat simulation system can be described as an abstract composition of description contracts.It becomes concrete at run time as services that implement the constituent description contracts are discovered and bind.The whole process is a continuous run-time activity that responds to simulation needs and the availability of services.This provides benefits of implementation transparency and minimal dependency between models.Thus,simulation system can minimize the impact of change on it and increase the overall efficiency to respond to requirements change.
文摘Value function approximation plays an important role in reinforcement learning(RL)with continuous state space,which is widely used to build decision models in practice.Many traditional approaches require experienced designers to manually specify the formulization of the approximating function,leading to the rigid,non-adaptive representation of the value function.To address this problem,a novel Q-value function approximation method named‘Hierarchical fuzzy Adaptive Resonance Theory’(HiART)is proposed in this paper.HiART is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input automatically.HiART begins with a highly generalized structure where the number of the category nodes is limited,which is beneficial to speed up the learning process at the early stage.Then,the network is refined gradually by creating the attached subnetworks,and a layered network structure is formed during this process.Based on this adaptive structure,HiART alleviates the dependence on expert experience to design the network parameter.The effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation time.Finally,a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART.
