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Text understanding and reasoning are among the core areas of artificial intelligence. Even a total solution to automatic text understanding and reasoning is still beyond the current techniques, thus it is time to build the stepping stones to solve a more straightforward problem set like question-answering (QA) without ambiguous utterances in the contexts or questions. The reported state-of-the-art approaches to this kind of problem are nearly all connectionist models based on neural networks. Significant progress has even been made in this direction. It is still hard for the pure connectionist models to handle logical reasoning. They generally suffer from the longstanding drawback of poor explainability and sensitivity to data noise and distribution. In this paper, we propose a complementary symbolic approach, GMR (Graph Matching based Reasoner) to QA — it automatically generates reasoning rules in the form of graphs from the training set and uses the generated rules to infer answers to the questions in the test set via graph matching. By employing this symbolic approach, 20 tasks in bAbI are solved with an average accuracy of 99.38%, and it outperforms the state-of-the-art for a real-life QA dataset WikiTableQuestions. After analyzing the accuracy of the basic evaluation indicators, we studied the generalization ability of the model in the paper, including the anti-noise ability, the convergence of the model, the stability of the model, the complexity of the algorithm, and the uncertainty of the parameters. Through comprehensive analysis and comparison, our model is stronger than the neural network model regarding anti-noise interference. Compared with the neural network model, our model performs very well in multi-tasking, and the stability of the model is quite high. The diversity of tasks did not reduce the stability of the model. We have conducted a comprehensive analysis and comparison of the parameter uncertainties. Our model can optimally select parameter configurations and will not cause a sharp drop in performance due to the parameter uncertainties. Finally, we describe the complexity of the GMR method and the optimal configuration of its parameters. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid intelligent tracking control approach is developed to address optimal tracking problems for a class of nonlinear discrete-time systems. The generalized value iteration algorithm is utilized to attain the admissible tracking control with off-line training, while the on-line near-optimal control method is established to enhance the control performance. It is emphasized that the value iteration performance is improved by introducing the acceleration factor. By collecting the input–output data of the unknown system plant, the model neural network is constructed to provide the partial derivative of the system state with respect to the control law as the approximate control matrix. A novel computational strategy is introduced to obtain the steady control of the reference trajectory. The critic and action neural networks are utilized to approximate the cost function and the tracking control, respectively. Considering approximation errors of neural networks, the stability analysis of the specific systems is provided via the Lyapunov approach. Finally, two numerical examples with industrial application backgrounds are involved for verifying the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper focuses on developing the system for people flow recognition based on many video camera images. The project arose based on the real need — its application in the city malls to find the most visited places and support customers' campaigns. The potential application in other domains is possible (public buildings, airports). In contrast to the existing solutions, our approach involves on-edge image analysis to decrease the risk of data loss and the system cost. In the project, we design the whole processing pipeline composed of the component modules responsible for detecting, tracking, and reidentifying (shuffling) people. Our experimental platform enabled us to compare multiple method variants for each module. Based on extensive experimental research, the final solution uses the pretrained SSD method in the detection module. The centroid algorithm applied to displacement vectors combined with the Siamese network is the basis for the object tracking module. The best model to solve the reidentification task is the Resnet50. For the Market 1501 dataset, it achieved Rank-1 efficiency of 84.6%. The system gives a visualization of the main paths of people's movements in the form of a heat map and assigns the direction where people most often look. In the experimental study, we assessed the system's effectiveness and time efficiency, and the current results give a perspective for its commercialization in the nearest future. • Development of the whole processing pipeline of human flow and reidentification. • Research aiming at the choice of competitive component methods • Experimental tuning of methods to achieve the high efficiency and low processing time. • Extension of the centroids algorithm with displacement vectors. • Platform for easy testing various methods of detection and reidentification. [ABSTRACT FROM AUTHOR]