The property of NP_completeness of topologic spatial reasoning problem has been proved.According to the similarity of uncertainty with topologic spatial reasoning,the problem of directional spatial reasoning should be...The property of NP_completeness of topologic spatial reasoning problem has been proved.According to the similarity of uncertainty with topologic spatial reasoning,the problem of directional spatial reasoning should be also an NP_complete problem.The proof for the property of NP_completeness in directional spatial reasoning problem is based on two important transformations.After these transformations,a spatial configuration has been constructed based on directional constraints,and the property of NP_completeness in directional spatial reasoning has been proved with the help of the consistency of the constraints in the configuration.展开更多
It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has...It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has been done when all variables are in a known directed acyclic graph(DAG). However, steady directed cyclic graphs(DCGs) may be involved when we simply combine modules containing local data together, where a module is composed of a child variable and its parent variables. So far, the physical and statistical meaning of steady DCGs remain unclear and unsolved. This paper illustrates the physical and statistical meaning of steady DCGs, and presents a method to calculate the JPD with local data, given that all variables are in a known single-valued Dynamic Uncertain Causality Graph(S-DUCG), and thus defines a new Bayesian Network with steady DCGs. The so-called single-valued means that only the causes of the true state of a variable are specified, while the false state is the complement of the true state.展开更多
文摘The property of NP_completeness of topologic spatial reasoning problem has been proved.According to the similarity of uncertainty with topologic spatial reasoning,the problem of directional spatial reasoning should be also an NP_complete problem.The proof for the property of NP_completeness in directional spatial reasoning problem is based on two important transformations.After these transformations,a spatial configuration has been constructed based on directional constraints,and the property of NP_completeness in directional spatial reasoning has been proved with the help of the consistency of the constraints in the configuration.
基金supported by the National Natural Science Foundation of China under Grant 71671103
文摘It is desired to obtain the joint probability distribution(JPD) over a set of random variables with local data, so as to avoid the hard work to collect statistical data in the scale of all variables. A lot of work has been done when all variables are in a known directed acyclic graph(DAG). However, steady directed cyclic graphs(DCGs) may be involved when we simply combine modules containing local data together, where a module is composed of a child variable and its parent variables. So far, the physical and statistical meaning of steady DCGs remain unclear and unsolved. This paper illustrates the physical and statistical meaning of steady DCGs, and presents a method to calculate the JPD with local data, given that all variables are in a known single-valued Dynamic Uncertain Causality Graph(S-DUCG), and thus defines a new Bayesian Network with steady DCGs. The so-called single-valued means that only the causes of the true state of a variable are specified, while the false state is the complement of the true state.
