A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block mode...A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.展开更多
Understanding how population sizes vary over time is a key aspect of ecological research. Unfortunately, our under- standing of population dynamics has historically been based on an assumption that individuals are ide...Understanding how population sizes vary over time is a key aspect of ecological research. Unfortunately, our under- standing of population dynamics has historically been based on an assumption that individuals are identical with homogenous life-history properties. This assumption is certainly false for most natural systems, raising the question of what role individual variation plays in the dynamics of populations. While there has been an increase of interest regarding the effects of within popula- tion variation on the dynamics of single populations, there has been little study of the effects of differences in within population variation on patterns observed across populations. We found that life-history differences （clutch size） among individuals ex- plained the majority of the variation observed in the degree to which population sizes of eastern fence lizards Sceloporus undula- tus fluctuated. This finding suggests that differences across populations cannot be understood without an examination of differences at the level of a system rather than at the level of the individual展开更多
文摘A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.
文摘Understanding how population sizes vary over time is a key aspect of ecological research. Unfortunately, our under- standing of population dynamics has historically been based on an assumption that individuals are identical with homogenous life-history properties. This assumption is certainly false for most natural systems, raising the question of what role individual variation plays in the dynamics of populations. While there has been an increase of interest regarding the effects of within popula- tion variation on the dynamics of single populations, there has been little study of the effects of differences in within population variation on patterns observed across populations. We found that life-history differences （clutch size） among individuals ex- plained the majority of the variation observed in the degree to which population sizes of eastern fence lizards Sceloporus undula- tus fluctuated. This finding suggests that differences across populations cannot be understood without an examination of differences at the level of a system rather than at the level of the individual
