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Guoquan Liu

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2 papers

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EAAI Journal 2026 Journal Article

Contrastive generative learning for enhanced multiagent decision-making under uncertainty

Yinghui Pan
Xinyi Xiang
Yifeng Zeng
Biyang Ma
Guoquan Liu
Yew-Soon Ong

This paper investigates the complexity of intelligent decision-making within multiagent systems operating under uncertainty, particularly emphasizing the challenges associated with modeling behaviors of other agents and optimizing decision-making for a subject agent in a common environment characterized by incomplete historical data. To address these challenges, we propose a generative learning method based on a general multiagent decision making framework, namely interactive dynamic influence diagrams, and apply contrastive learning to diversify the generation of potential behaviors, thereby enhancing the subject agent’s modeling and prediction capabilities. We conduct experiments on multiple classic domains to demonstrate the efficacy of the new learning method in improving decision-making quality. The empirical results highlight its substantial improvement in enhancing overall performance in multiagent decision-making. Our work contributes to multiagent decision making particularly when a subject agent interacts with other unknown agents, including humans, in many practical applications.

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NeurIPS Conference 2024 Conference Paper

An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling

Shenbao Yu
Yinghui Pan
Yifeng Zeng
Prashant Doshi
Guoquan Liu
Kim-Leng Poh
Mingwei Lin

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

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