Student cognitive modeling plays an important role in the rapid development of educational data mining research. It aims to discover students' proficiency in knowledge concepts as well as to predict students' performance in conducting exercises. Studies in the past few years have been mainly centered around two types of techniques: cognitive diagnosis models and data mining approaches. Cognitive diagnosis models focus on students' cognitive states and assess their knowledge concept proficiency through handcrafted features. The subjective features may trigger cascading errors in the students' performance prediction. On the other hand, data mining techniques, e.g., matrix factorization methods, achieve high prediction accuracy by directly modeling the students' exercising process. It lacks measuring the students' knowledge concept proficiency. To address the dilemma of the aforementioned methods, in this paper, we propose a scalable non-negative matrix co-factorization (SNMCF) model by jointly modeling the students' knowledge states and their exercising process. SNMCF can achieve high accuracy in predicting students' exercise performance while modeling their states of knowledge concepts in a given domain. We conduct extensive experiments on several real-world datasets, including large sparse ones, and demonstrate the effectiveness of our new approach in terms of prediction accuracy, cognitive diagnostic ability, and scalability

Discovering common intentions of multiple agents is one of the important ways to detect the tendency of their collaborative behaviours. Existing work mainly focuses on intention recognition in a single-agent setting and uses a descriptive model, e.g. Bayesian networks, in the recognition process. In this article, we develop a new approach of identifying intentions for multiple agents through analysing their behaviours over time. We first define a prescriptive, behavioural model for a single agent that represents the agent’s behaviours where their intentions are hidden in the plan execution. We introduce landmarks into the behavioural model therefore enhancing informative features to identify common intentions for multiple agents. Subsequently, we refine the model by focusing only on action sequences in their plans and provide a light model for identifying and comparing their intentions. The new model provides a simple approach of grouping agents’ common intentions upon partial plans observed in agents’ interactions. After that, we transform the intention recognition into an un-supervised learning problem and adapt a clustering algorithm to group intentions of multiple agents through comparing their behavioural models. We conduct the clustering process through measuring similarity of probability distributions over potential landmarks in the behavioural models so as to discover agents’ common intentions. Finally, we examine the new intention recognition approaches in two problem domains. We demonstrate importance of recognising common intentions of multiple agents in achieving their goals and provide experimental results to show performance of the new approaches.

In this paper, we study the problem of constrained path search with submodular function maximization (CPS-SM). We aim to find the path with the best submodular function score under a given constraint (e.g., a length limit), where the submodular function score is computed over the set of nodes in this path. This problem can be used in many applications. For example, tourists may want to search the most diversified path (e.g., a path passing by the most diverse facilities such as parks and museums) given that the traveling time is less than 6 hours. We show that the CPS-SM problem is NP-hard. We first propose a concept called “submodular$\alpha$-dominance” by utilizing the submodular function properties, and we develop an algorithm with a guaranteed error bound based on this concept. By relaxing the submodular$\alpha$-dominance conditions, we design another more efficient algorithm that has the same error bound. We also utilize the way of bi-directional path search to further improve the efficiency of the algorithms. We finally propose a heuristic algorithm that is efficient yet effective in practice. The experiments conducted on several real datasets show that our proposed algorithms can achieve high accuracy and are faster than one state-of-the-art method by orders of magnitude.

Modeling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimize its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modeling insufficiency occurs when the agents are competitive and the subject agent cannot get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. Most of the recent research still assumes that the agents have common knowledge about their environments and a subject agent has the true behavior of other agents in its mind. Consequently, the resulting techniques are not applicable in many practical problem domains. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model before their interactions. The challenges lie in generating and measuring new behaviors of other agents. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top- K $K$ behaviors. We demonstrate the performance of the new techniques in two well-studied problem domains. The top- K $K$ behavior selection embarks the study of unknown behaviors in multiagent decision making and inspires investigation of diversifying agents' behaviors in competitive agent interactions. This study will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world.

Abstract With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.

The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data.

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing “Word-of-Mouth” effect in social networks. Time plays an important role in the influence spread from one user to another and the time needed for a user to influence another varies. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm. To improve the algorithm scalability, we propose the concept of Influence Spreading Path in social networks and develop a set of new algorithms for the time constrained influence maximization problem. We further parallelize the algorithms for achieving more time savings. Additionally, we generalize the proposed algorithms for the conventional influence maximization problem without time constraints. All of the algorithms are evaluated over four public available datasets. The experimental results demonstrate the efficiency and effectiveness of the algorithms for both conventional influence maximization problem and its time constrained version.

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing "Word-of-Mouth" effect in social networks. Influence maximization problem has been extensively studied recently. However, none of the previous work considers the time constraint in the influence maximization problem. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm with performance guarantees. To improve the algorithm scalability, we propose two Influence Spreading Path based methods. Extensive experiments conducted over four public available datasets demonstrate the efficiency and effectiveness of the Influence Spreading Path based methods.

Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data.

Multiply sectioned influence diagrams (MSID), together with hyper relevance graph (HRG), is a probabilistic graphical decision model representing distributed decision problems involving multiple agents. To ensure a valid model representation, an MSID observes a set of constraints that must be verified before its evaluation. We present a symbolic method to verify some constraints of this graphical decision model in a cooperative fashion. The method makes full use of an algebraic description of factorization joint probability in an MSID instead of focusing on the graphical structure of decision model. It is a cooperative verification that protects agent's privacy in multiagent systems. Furthermore, the issue of verification failure is investigated with some helpful corrections. Finally, a pairwise verification is proposed concerning other constraints to realize a valid model associated with the knowledge domain. © 2008 Wiley Periodicals, Inc.