Abstract: A reasonable task analysis can help decision makers to plan tasks quickly and accurately.The current task analysis method using case-based reasoning has problems such as long analysis time and low accuracy of analysis results.The method uses LSTM as the generator and RNN as the discriminator.For failing to return updates for small gradients of discrete data,the ge-nerator uses rollout policy to complete the incomplete sequence generated and the discriminator uses Monte Carlo(MC) to sample the data to obtain the complete data sequence action value function,thus guiding on updating the parameters of the generator.To address the problems of sparse data with obscure features and unclear data focus,a soft attention mechanism is added to the GAN before training.It assigns different weights to different features to filter out redundant data and select the important features.The proposed method is compared with the GAN without the attention mechanism on the same simulated dataset,and it is demonstrated that the method with the attention mechanism improves 0.088,0.092,0.094 and 0.068 in terms of P,R,F1 value and accuracy respectively.Compared with other neural network recommendation algorithms, P,R,F1 valueand accuracy is improvedby 0.1~0.3,0.1~0.2,0.1~0.25 and 0.07~0.17,respectively,which proves the effectiveness of the proposed method.

Key words: Attentional mechanisms, GAN, Task analysis, RNN, Task recommendation