Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent r...Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.展开更多
Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement learning.However,current methods that address the overestimation problem tend to introduce underestimation,w...Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement learning.However,current methods that address the overestimation problem tend to introduce underestimation,which face a challenge of precise decision-making in many fields.To address this issue,we conduct a theoretical analysis of the underestimation bias and propose the minmax operation,which allow for flexible control of the estimation bias.Specifically,we select the maximum value of each action from multiple parallel state-action networks to create a new state-action value sequence.Then,a minimum value is selected to obtain more accurate value estimations.Moreover,based on the minmax operation,we propose two novel algorithms by combining Deep Q-Network(DQN)and Double DQN(DDQN),named minmax-DQN and minmax-DDQN.Meanwhile,we conduct theoretical analyses of the estimation bias and variance caused by our proposed minmax operation,which show that this operation significantly improves both underestimation and overestimation biases and leads to the unbiased estimation.Furthermore,the variance is also reduced,which is helpful to improve the network training stability.Finally,we conduct numerous comparative experiments in various environments,which empirically demonstrate the superiority of our method.展开更多
In the research of uncertain information processing,Dempster-Shafer Theory(DST)provides a framework for dealing with uncertain information,where evidence is defined on a Frame of Discernment(FOD)consisting of mutually...In the research of uncertain information processing,Dempster-Shafer Theory(DST)provides a framework for dealing with uncertain information,where evidence is defined on a Frame of Discernment(FOD)consisting of mutually exclusive elements.However,the requirement of exclusiveness on FOD sometimes is not satisfied,as shown in Dezert-Smarandache Theory(DSm T),a derivative of DST.In DSm T,the non-exclusiveness is expressed by propositions’intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution(PCR)rule.In order to handle non-exclusive FODs,a new framework called D Number Theory(DNT)has been proposed recently,which quantifies the non-exclusive degree between propositions different from DSm T.In previous studies,an Exclusive Conflict Redistribution(ECR)rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD,but there are some deficiencies in the ECR rule.In this paper,a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a nonexclusive FOD.Within the proposed rule,the definition of conflict utilizes the idea of ECR’s exclusive conflict,and the disposal of conflict is following the idea of PCR’s proportional redistribution.Properties of the ECR-PCR rule are presented.The effectiveness of the proposed new rule is verified through numerical examples and applications,in comparison with other fusion methods.展开更多
基金partially supported by the National Natural Science Foundation of China(No.62173272)。
文摘Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.
基金supported by the National Natural Science Foundation of China(No.62173272).
文摘Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement learning.However,current methods that address the overestimation problem tend to introduce underestimation,which face a challenge of precise decision-making in many fields.To address this issue,we conduct a theoretical analysis of the underestimation bias and propose the minmax operation,which allow for flexible control of the estimation bias.Specifically,we select the maximum value of each action from multiple parallel state-action networks to create a new state-action value sequence.Then,a minimum value is selected to obtain more accurate value estimations.Moreover,based on the minmax operation,we propose two novel algorithms by combining Deep Q-Network(DQN)and Double DQN(DDQN),named minmax-DQN and minmax-DDQN.Meanwhile,we conduct theoretical analyses of the estimation bias and variance caused by our proposed minmax operation,which show that this operation significantly improves both underestimation and overestimation biases and leads to the unbiased estimation.Furthermore,the variance is also reduced,which is helpful to improve the network training stability.Finally,we conduct numerous comparative experiments in various environments,which empirically demonstrate the superiority of our method.
基金partially supported by the National Natural Science Foundation of China(No.61703338)。
文摘In the research of uncertain information processing,Dempster-Shafer Theory(DST)provides a framework for dealing with uncertain information,where evidence is defined on a Frame of Discernment(FOD)consisting of mutually exclusive elements.However,the requirement of exclusiveness on FOD sometimes is not satisfied,as shown in Dezert-Smarandache Theory(DSm T),a derivative of DST.In DSm T,the non-exclusiveness is expressed by propositions’intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution(PCR)rule.In order to handle non-exclusive FODs,a new framework called D Number Theory(DNT)has been proposed recently,which quantifies the non-exclusive degree between propositions different from DSm T.In previous studies,an Exclusive Conflict Redistribution(ECR)rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD,but there are some deficiencies in the ECR rule.In this paper,a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a nonexclusive FOD.Within the proposed rule,the definition of conflict utilizes the idea of ECR’s exclusive conflict,and the disposal of conflict is following the idea of PCR’s proportional redistribution.Properties of the ECR-PCR rule are presented.The effectiveness of the proposed new rule is verified through numerical examples and applications,in comparison with other fusion methods.
