An integration-centric approach is proposed to handle inadequate information in the system readiness level （SRL） assessment using the evidential reasoning （ER） algorithm. Current SRL assessment approaches cannot b...An integration-centric approach is proposed to handle inadequate information in the system readiness level （SRL） assessment using the evidential reasoning （ER） algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system＇s structure. The importance of the system＇s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.展开更多
国际笔会和西班牙拉曼·鲁尔学院于2007年联手推出了《译还是不译——国际文学翻译形势报告》(To Be Translated or Not To Be——Pen/IRL Report on the International Situation of Literary Translation),探讨了全球化背景下国...国际笔会和西班牙拉曼·鲁尔学院于2007年联手推出了《译还是不译——国际文学翻译形势报告》(To Be Translated or Not To Be——Pen/IRL Report on the International Situation of Literary Translation),探讨了全球化背景下国际文学翻译的现状和问题,特别是各国文学译入到英语的问题。本文对该报告的内容进行了解读,并在此基础上分析了该报告对中国文学走出去的启示:中国政府实施中国文化走出去的战略决定顺应了时代发展的潮流;但对"怎么走出去","如何更有效地走出去",以及"走出去的效果如何"等尚缺乏足够的理论探讨和实证研究。展开更多
Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-f...Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.展开更多
Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent performance.Reinforcement Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential ...Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent performance.Reinforcement Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision problem.An intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance unstable.Current mainstream methods apply heuristic reward functions to counter this challenge.However,the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object entities.To this end,we propose a novel adaptive Inverse Reinforcement Learning(IRL)framework for multi-hop reasoning,called AInvR.(1)To counter the missing and spurious paths,we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories;(2)to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules,we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy;(3)to further explore diverse paths and mitigate the influence of missing facts,we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning process.Experimental results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.展开更多
基金supported by the National Natural Science Foundation of China (70901074 71001104)
文摘An integration-centric approach is proposed to handle inadequate information in the system readiness level （SRL） assessment using the evidential reasoning （ER） algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system＇s structure. The importance of the system＇s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.
文摘国际笔会和西班牙拉曼·鲁尔学院于2007年联手推出了《译还是不译——国际文学翻译形势报告》(To Be Translated or Not To Be——Pen/IRL Report on the International Situation of Literary Translation),探讨了全球化背景下国际文学翻译的现状和问题,特别是各国文学译入到英语的问题。本文对该报告的内容进行了解读,并在此基础上分析了该报告对中国文学走出去的启示:中国政府实施中国文化走出去的战略决定顺应了时代发展的潮流;但对"怎么走出去","如何更有效地走出去",以及"走出去的效果如何"等尚缺乏足够的理论探讨和实证研究。
文摘Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
基金This work was supported by the National Natural Science Foundation of China(No.U19A2059)。
文摘Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent performance.Reinforcement Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision problem.An intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance unstable.Current mainstream methods apply heuristic reward functions to counter this challenge.However,the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object entities.To this end,we propose a novel adaptive Inverse Reinforcement Learning(IRL)framework for multi-hop reasoning,called AInvR.(1)To counter the missing and spurious paths,we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories;(2)to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules,we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy;(3)to further explore diverse paths and mitigate the influence of missing facts,we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning process.Experimental results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.
