A Knowledge-Based Neurocomputing Decision Support System for Biodiversity and Conservation Biology

摘要

We present an artificial intelligence method for the development of decision support systems for environmental management and demonstrate its strengths using an example from the domain of biodiversity and conservation biology. Renosterveld is a vegetation unique to South Africa; it is under threat of extinction as a result of rapidly growing agricultural activities. Our approach takes into account local expert knowledge together with collected field data about plant habitats in order to identify areas which show potential for conserving thriving areas of Renosterveld vegetation and areas that are best suited for agriculture and combines them in a knowledge-based neural network. The paradigm provides means for using prior knowledge to determine a suitable neural network architecture, to program a subset of weights to induce an explicit learning bias which guides network training, and to extract knowledge from trained networks. The role of neural networks then becomes that of knowledge refinement. It thus provides a methodology for dealing with uncertainty in an initial domain theory. We present a quantitative solution to the determination of the learning bias which takes the network architecture, the prior knowledge, the training data and the gradient descent neural network learning algorithm into consideration.