Springer Science and Business Media LLC

Model-based Diagnosis is a well known AI technique that has been applied to software debugging for senior programmers, called Model-Based Software Debugging (MBSD). In this paper we describe the basis of MBSD and show how it can be used for educational purposes. By extending the classical diagnosis technique to a hierarchical approach, we built a programming learning system to allow a student to debug his program in different abstraction levels.

Group decision making (GDM) can make full use of information in the group and consider perspectives of multiple stakeholders, which matches the characteristic of enterprise human resource (HR) allocation. This paper tries to optimize the enterprise HR allocation by establishing a fair-satisfied-based GDM model with two objectives of fairness and satisfaction respectively. Firstly, double hierarchy linguistic term set is used to collect the evaluation information of decision makers (DMs). Then, two quantification methods for DMs’ satisfaction perception and fairness perception are respectively provided respectively based on the Prospect theory. Moreover, combining with the Pareto optimal idea, the fair-satisfied-based GDM model is designed and applied to deal with an enterprise HR allocation problem. Finally, some comprehensive analyses are made to validate the proposed method.

Vehicle routing (VRP) and traveling salesman problems (TSP) are classical and interesting NP-hard routing combinatorial optimization (CO) with practical significance. While moving forward with artificial intelligence, researchers are paying more and more attention to applying machine learning to classical CO problems. However, traditional reinforcement learning faces challenges like reward sparsity and unstable training, so it is necessary to assist agents in finding high-quality routings in the initial model training stage to obtain more positive feedback. This paper proposes a novel Monte Carlo Tree Search (MCTS)-based two-stage multi-agent reinforcement learning training pipeline (MCRL) in which we also design a multifunctional reward function, improving efficiency, accuracy, and diversity to guide agents to learn the routings over graphs better. Besides, previous approaches are frequently too sluggish in runtime to be useful in contexts with sparsely connected networks and uncertain traffic. As an alternative, we design a model based on graph neural networks that can execute multi-agent routing in a sparsely connected graph with constantly changing traffic circumstances. Also, the agents are better equipped to collaborate online and adjust to changes thanks to our learned communication module.

In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top-k recommendation.

Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.

Multi-class contour preserving classification is a contour conservancy technique that synthesizes two types of vectors; fundamental multi-class outpost vectors (FMCOVs) and additional multi-class outpost vectors (AMCOVs), at the judging border between classes of data to improve the classification accuracy of the feed-forward neural network. However, the number of both new vectors is tremendous, resulting in a significantly prolonged training time. Reduced multi-class contour preserving classification provides three practical methods to lessen the number of FMCOVs and AMCOVs. Nevertheless, the three reduced multi-class outpost vector methods are serial and therefore have limited applicability on modern machines with multiple CPU cores or processors. This paper presents the methodologies and the frameworks of the three parallel reduced multi-class outpost vector methods that can effectively utilize thread-level parallelism and process-level parallelism to (1) substantially lessen the number of FMCOVs and AMCOVs, (2) efficiently increase the speedups in execution times to be proportional to the number of available CPU cores or processors, and (3) significantly increase the classification performance (accuracy, precision, recall, and F1 score) of the feed-forward neural network. The experiments carried out on the balanced and imbalanced real-world multi-class data sets downloaded from the UCI machine learning repository confirmed the reduction performance, the speedups, and the classification performance aforementioned.

Representation-based methods have achieved exciting results in recent applications of face recognition. However, it is still challenging for the face recognition task due to noise and outliers in the data. Many existing methods avoid these problems by constructing an auxiliary dictionary from the extended data but fail to achieve good performances because they use the main dictionary only for classification. In this paper, to avoid the need to manually construct an auxiliary dictionary and the effects of noise, we propose a Joint Latent Low-Rank and Non-Negative Induced Sparse Representation (JLSRC) for face recognition. Specifically, JLSRC adaptively learns two clean low-rank reconstructed dictionaries jointly via an extended latent low-rank representation to reveal the potential relationships in the data and then embeds a non-negative constraint and an Elastic Net regularization in the coefficient vectors of the dictionaries to enhance the performance on classification. In this way, the learned low-rank dictionaries can be mutually boosted to extract discriminative features and handle the noise, and the obtained coefficient vectors are simultaneously both sparse and discriminative. Moreover, the proposed method seamlessly and elegantly integrates low-rank learning and sparse representation-based classification. Extensive experiments on three challenging face databases demonstrate the effectiveness and robustness of JLSRC in comparison with the state-of-the-art methods.