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Quanlong Guan

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16 papers

2 author rows

AAAI Conference 2026 Conference Paper

Generating In-Distribution Counterfactual Explanation for Graph Neural Networks

Linmao Chen
Chaobo He
Junwei Cheng
Chunying Li
Quanlong Guan

Graph Neural Networks (GNNs) have received increasing attention due to their ability to handle graph-structured data, yet their explainability remains a significant challenge. An effective solution is to provide the GNN models with counterfactual explanations, which aim to answer “How should the input instance be perturbed to change the model's prediction?". However, existing works mainly focus on generating explanations that can effectively alter model predictions, while neglecting whether the explanations remain aligned with the original data distribution, leading to the distribution shift problem. To address this problem, we propose a novel method called ICExplainer for generating explanations within the original distribution. Specifically, we introduce graph diffusion-based generative model into the counterfactual reasoning, treating it as an optimization objective for graph distribution learning. Taking insights from variational inference, we use it to estimate the true distribution of the input graphs to retain essential structural and semantic information. The inferred distribution is then utilized as prior knowledge to guide the reverse process, ensuring that generated explanations are both counterfactual and distributionally coherent. Extensive experiments conducted on both synthetic and real-world datasets demonstrate the superior performance of ICExplainer over existing methods.

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AAAI Conference 2026 Conference Paper

GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation

Xinghe Cheng
Zihan Zhang
Jiapu Wang
Liangda Fang
Chaobo He
Quanlong Guan
Shirui Pan
Weiqi Luo

Learning path recommendation seeks to provide students with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relations, which present two major limitations: (1) Prerequisite relations between knowledge concepts are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. (2) Relying on a single sequentially dependent knowledge structure based on prerequisite relations implies that a confusing knowledge concept can disrupt subsequent learning processes, which is referred to as blocked learning. To address these two challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relations between knowledge concepts. Specifically, we introduce a knowledge structure graph generation module EDU-GraphRAG that constructs knowledge structure graphs for different educational datasets, significantly improving the applicability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module that utilizes similarity relations as fallback relations when prerequisite relations become ineffective, thereby alleviating the blocked learning. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also generates more effective and longer learning paths.

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AAAI Conference 2025 Conference Paper

A Syntactic Approach to Computing Complete and Sound Abstraction in the Situation Calculus

Liangda Fang
Xiaoman Wang
Zhang Chen
Kailun Luo
Zhenhe Cui
Quanlong Guan

Abstraction is an important and useful concept in the field of artificial intelligence. To the best of our knowledge, there is no syntactic method to compute a sound and complete abstraction from a given low-level basic action theory and a refinement mapping. This paper aims to address this issue. To this end, we first present a variant of situation calculus, namely linear integer situation calculus, which serves as the formalization of high-level basic action theory. We then migrate Banihashemi, De Giacomo, and Lesperance’s abstraction framework to one from linear integer situation calculus to extended situation calculus. Furthermore, we identify a class of Golog programs, namely guarded actions, so as to restrict low-level Golog programs, and impose some restrictions on refinement mappings. Finally, we design a syntactic approach to computing a sound and complete abstraction from a low-level basic action theory and a restricted refinement mapping.

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AAMAS Conference 2025 Conference Paper

Automatic Verification of Linear Integer Planning Programs via Forgetting in LIAUPF

Liangda Fang
Shikang Chen
Xiaoman Wang
Xiaoyou Lin
Chenyi Zhang
Qingliang Chen
Quanlong Guan
Kaile Su

The goal of generalized planning (GP) is to find a generalized solution for a class of planning problems. One of effective means to solve GP is to transform a GP problem into an abstract planning problem, which can be easily solved. Recently, Lin et al. proposed a novel abstract model for GP, namely generalized linear integer numeric planning (GLINP), whose solution is an algorithmic-like structure called a planning program. They also developed an inductive approach to generating planning programs for GLINP. However, it has no theoretical guarantee that the generated planning program holds for infinitely many problem instances. To address this defect, we propose an automatic approach to verify whether the planning program works for infinitely many problem instances in this paper. We translate the planning program into a set of trace axioms finitely represented by linear integer arithmetic with uninterpreted predicate and function symbols (LIAUPF), and reduce the problem to the entailment problem of LIAUPF. Due to the undecidability of entailment problem in LIAUPF, we identify a class of planning programs whose trace axioms can be simplified in linear integer arithmetic (LIA), that is, a decidable fragment of LIAUPF, when reasoning about only the input and output of planning programs. As a result, the correctness verification of this class of programs becomes decidable.

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UAI Conference 2025 Conference Paper

DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction

Weixiong Liu
Junwei Cheng
Zhongyu Pan
Chaobo He
Quanlong Guan

Dynamic link prediction (DLP) is a crucial task in graph learning, aiming to predict future links between nodes at subsequent time in dynamic graphs. Recently, graph masked autoencoders (GMAEs) have shown promising performance in self-supervised learning. However, their application to DLP is under-explored. Existing GMAEs struggle to capture temporal dependencies, and their random masking causes crucial information loss for DLP. Moreover, most existing DLP methods rely on local information, ignoring global information and failing to capture complex features in real-world dynamic graphs. To address these issues, we propose DyGMAE, a novel dynamic GMAE method specifically designed for DLP. DyGMAE introduces a Multi-Scale Masking Strategy (MSMS), which generates multiple graph views by masking parts of the edges and tries to reconstruct them. Additionally, a multi-scale masking representation alignment module with a contrastive learning objective is employed to align representations which are encoded by unmasked edges across these views. Through this design, different masked views can provide diverse information to alleviate the drawbacks of random masking, and contrastive learning can align different views to mitigate the problem of exploiting local and global information simultaneously. Experiments on benchmark datasets show DyGMAE achieves superior performance in the DLP task.

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NeurIPS Conference 2025 Conference Paper

FerretNet: Efficient Synthetic Image Detection via Local Pixel Dependencies

Shuqiao Liang
Jian Liu
Chen Renzhang
Quanlong Guan

The increasing realism of synthetic images generated by advanced models such as VAEs, GANs, and LDMs poses significant challenges for synthetic image detection. To address this issue, we explore two artifact types introduced during the generation process: (1) latent distribution deviations and (2) decoding-induced smoothing effects, which manifest as inconsistencies in local textures, edges, and color transitions. Leveraging local pixel dependencies (LPD) properties rooted in Markov Random Fields, we reconstruct synthetic images using neighboring pixel information to expose disruptions in texture continuity and edge coherence. Building upon LPD, we propose FerretNet, a lightweight neural network with only 1. 1M parameters that delivers efficient and robust synthetic image detection. Extensive experiments demonstrate that FerretNet—trained exclusively on the 4-class ProGAN dataset—achieves an average accuracy of 97. 1% on an open-world benchmark comprising 22 generative models. Our code and datasets are publicly available at https: //github. com/xigua7105/FerretNet.

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IJCAI Conference 2025 Conference Paper

Improvements to the Generate-and-Complete Approach to Conformant Planning

Liangda Fang
Min Zhan
Jin Tong
Xiujie Huang
Ziliang Chen
Quanlong Guan

Conformant planning is a computationally challenging task that generates an action sequence to achieve goal condition with uncertain initial states and non-deterministic actions. The generate-and-complete (in short, GC) approach shows superior performance on conformant planning, which iteratively enumerates the solution of a planning subproblem for a single initial state and attempts to extend it for all initial states until a conform solution is found. However, two major drawbacks of the GC approach hinder its performance: the computational overhead due to state exploration and the insertion of many redundant actions. To overcome the above drawbacks, we improve both verification and completion procedures. Experimental results show that the improved GC planner has significant improvements over the original GC approach in many instances with a large number of initial states. Our approach also outperforms all of state-of-the-art planners, solving 989 instances in comparison to 784, which is the most solved by DNF.

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IROS Conference 2025 Conference Paper

Scalable MARL for Cooperative Exploration with Dynamic Robot Populations via Graph-Based Information Aggregation

Xiaoqi Ren
Guanglong Du
Zhuoyao Wang 0001
Dong Xu
Xueqian Wang
Quanlong Guan
Xiaojian Qiu

This study addresses the challenge of multi-robot cooperative exploration under limited local observations in environments with dynamic robot populations. To achieve efficient area coverage within constrained timeframes, we propose the Multi-Robot Informative Planner (MIP), a novel reinforcement learning (RL)-based planning module. The core component of MIP is the Neighborhood Information Aggregator, which employs a graph neural network (GNN) to integrate local neighborhood information for each robot. Our design enhances sample efficiency by minimizing information requirements while ensuring scalability across environments with varying robot numbers. To generate high-quality, expressive neighborhood feature representations, we utilize Graphical Mutual Information (GMI) to maximize the correlation between neighboring robots’ input features and their high-level hidden representations. Furthermore, MIP incorporates the Spatial-Neighborhood Transformer, which captures spatial features and inter-robot interactions through spatial self-attention mechanisms. These components collectively form the Multi-Robot Neural Informative Mapping (MRNIM) framework, outperforming traditional benchmarks in Habitat simulator.

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IJCAI Conference 2024 Conference Paper

A Multi-Valued Decision Diagram-Based Approach to Constrained Optimal Path Problems over Directed Acyclic Graphs

Mingwei Zhang
Liangda Fang
Zhenhao Gu
Quanlong Guan
Yong Lai

Numerous combinatorial optimization problems can be reduced to the optimal path problem over directed acyclic graphs (DAGs). The constrained version of the optimal path problem requires the solution to satisfy a given logical constraint. BDD-constrained search (BCS) is an efficient algorithm for the constrained optimal path problem over DAGs. This algorithm considers edges as variables and constraints as Boolean functions and maintains constraints via binary decision diagrams (BDDs), a compact form of Boolean functions. However, BCS involves redundant operations during the search process. To reduce these redundant operations, we use vertices instead of edges as variables and hence represent constraints as multi-valued functions. Due to the multi-valued representation of constraints, we propose a novel algorithm, namely MDD-constrained search (MCS), by using multi-valued decision diagrams (MDDs) instead of BDDs, an efficient representation of multi-valued functions. In addition, we improve MCS via domain reduction in multi-valued functions. Experimental results prove that our proposed algorithm outperforms BCS.

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AAAI Conference 2024 Conference Paper

Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach

Ziliang Chen
Yongsen Zheng
Zhao-Rong Lai
Quanlong Guan
Liang Lin

Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels deconfounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical result verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. The fake invariance severely endangers OOD generalization since the trustful objective can not be diagnosed and existing causal remedies are invalid to rectify. In this paper, we review a IRL family (InvRat) under the Partially and Fully Informative Invariant Feature Structural Causal Models (PIIF SCM /FIIF SCM) respectively, to certify their weaknesses in representing fake invariant features, then, unify their causal diagrams to propose ReStructured SCM (RS-SCM). RS-SCM can ideally rebuild the spurious and the fake invariant features simultaneously. Given this, we further develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects. It can be easily implemented by a small feature selection subnet introduced in the IRL family, which is alternatively optimized to achieve our goal. Experiments verified the superiority of our approach to fight against the fake invariant issue across a variety of OOD generalization benchmarks.

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AAMAS Conference 2024 Conference Paper

Generalized Strategy Synthesis of Infinite-state Impartial Combinatorial Games via Exact Binary Classification

Liangda Fang
Meihong Yang
Dingliang Cheng
Yunlai Hao
Quanlong Guan
Liping Xiong

In game theory, a fundamental class of games is impartial combinatorial games (ICGs). One of the challenging and long-standing problems of ICGs is to compute generalized winning strategies for possibly infinite number of legal states. Recently, Wu et al. proposed an automated method to synthesize generalized winning strategies of infinite-state ICGs. Their method has two major drawbacks: (1) it fails to generate winning formula with large size; and (2) it cannot usually construct the winning strategy even the winning formula is obtained. To tackle the above two drawbacks, in this paper, we propose the problem of exact binary classification and design a partial MaxSAT-based method to this problem. Then, we reduce the synthesis problem of generalized winning strategies of infinite-state ICGs to exact binary classification. The experimental results show that our method is more scalable and effective than Wu et al. ’s approach.

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IJCAI Conference 2024 Conference Paper

On the Logic of Theory Change Iteration of KM-Update, Revised

Liangda Fang
Tong Zhu
Quanlong Guan
Junming Qiu
Zhao-Rong Lai
Weiqi Luo
Hai Wan

Belief revision and update, two significant types of belief change, both focus on how an agent modifies her beliefs in presence of new information. The most striking difference between them is that the former studies the change of beliefs in a static world while the latter concentrates on a dynamically-changing world. The famous AGM and KM postulates were proposed to capture rational belief revision and update, respectively. However, both of them are too permissive to exclude some unreasonable changes in the iteration. In response to this weakness, the DP postulates and its extensions for iterated belief revision were presented. Furthermore, Ferme and Goncalves integrated these postulates in belief update. Unfortunately, some redundant components are included in the definitions of belief states and the faithful assignments for semantic characterizations. Moreover, their approach does not meet the desired property of iterated belief update. They also do not discuss the rationale of any DP postulate within the update context. This paper is intended to fix these deficiencies of Ferme and Goncalves’s approach. Firstly, we present a modification of the original KM postulates based on belief states, and propose the notion of faithful collective assignments of belief states to partial preorders. Subsequently, we migrate several well-known postulates for iterated belief revision to iterated belief update. Moreover, we provide the exact semantic characterizations based on partial preorders for each of the proposed postulates. Finally, we analyze the compatibility between the above iterated postulates and the KM postulates for belief update.

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AIJ Journal 2024 Journal Article

On the role of logical separability in knowledge compilation

Junming Qiu
Wenqing Li
Liangda Fang
Quanlong Guan
Zhanhao Xiao
Zhao-Rong Lai
Qian Dong

Knowledge compilation is an alternative solution to address demanding reasoning tasks with high complexity via converting knowledge bases into a suitable target language. The notion of logical separability, proposed by Levesque, offers a general explanation for the tractability of clausal entailment for two remarkable languages: decomposable negation normal form and prime implicates. It is interesting to explore what role logical separability plays in problem tractability. In this paper, we apply the notion of logical separability to a number of reasoning problems within the context of propositional logic: satisfiability checking (CO), clausal entailment checking (CE), model counting (CT), model enumeration (ME) and forgetting (FO), as well as their dual tasks, contributing to several recursive procedures. We provide the corresponding logical separability based properties: CO-logical separability, CE-logical separability, CT-logical separability, ME-logical separability and their duals. Based on these properties, we then identify four novel normal forms: CO - LSNNF, CE - LSNNF, CT - LSNNF and ME - LSNNF, as well as their dual languages. We show that each of them is the necessary and sufficient condition under which the corresponding procedure is correct. We finally integrate the above normal forms into the knowledge compilation map.

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AAAI Conference 2024 Conference Paper

Unveiling the Tapestry of Automated Essay Scoring: A Comprehensive Investigation of Accuracy, Fairness, and Generalizability

Kaixun Yang
Mladen Raković
Yuyang Li
Quanlong Guan
Dragan Gašević
Guangliang Chen

Automatic Essay Scoring (AES) is a well-established educational pursuit that employs machine learning to evaluate student-authored essays. While much effort has been made in this area, current research primarily focuses on either (i) boosting the predictive accuracy of an AES model for a specific prompt (i.e., developing prompt-specific models), which often heavily relies on the use of the labeled data from the same target prompt; or (ii) assessing the applicability of AES models developed on non-target prompts to the intended target prompt (i.e., developing the AES models in a cross-prompt setting). Given the inherent bias in machine learning and its potential impact on marginalized groups, it is imperative to investigate whether such bias exists in current AES methods and, if identified, how it intervenes with an AES model's accuracy and generalizability. Thus, our study aimed to uncover the intricate relationship between an AES model's accuracy, fairness, and generalizability, contributing practical insights for developing effective AES models in real-world education. To this end, we meticulously selected nine prominent AES methods and evaluated their performance using seven distinct metrics on an open-sourced dataset, which contains over 25,000 essays and various demographic information about students such as gender, English language learner status, and economic status. Through extensive evaluations, we demonstrated that: (1) prompt-specific models tend to outperform their cross-prompt counterparts in terms of predictive accuracy; (2) prompt-specific models frequently exhibit a greater bias towards students of different economic statuses compared to cross-prompt models; (3) in the pursuit of generalizability, traditional machine learning models (e.g., SVM) coupled with carefully engineered features hold greater potential for achieving both high accuracy and fairness than complex neural network models.

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ICAPS Conference 2022 Conference Paper

Generalized Linear Integer Numeric Planning

Xiaoyou Lin
Qingliang Chen
Liangda Fang
Quanlong Guan
Weiqi Luo 0002
Kaile Su

Classical planning aims to find a sequence of actions that guarantees goal achievement from an initial state. The representative framework of classical planning is based on propositional logic. Due to the weak expressiveness of propositional logic, many applications of interest cannot be formalized as a classical planning problem. Some extensions such as numeric planning and generalized planning (GP) are therefore proposed. Qualitative numeric planning (QNP) is a decidable class of numeric and generalized extensions and serves as a numeric abstraction of GP. However, QNP is still far from being perfect and needs further improvement. In this paper, we introduce another generalized version of numeric planning, namely generalized linear integer numeric planning(GLINP), which is a more suitable abstract framework of GP than QNP. In addition, we develop a general framework to synthesize solutions to GLINP problems. Finally, we evaluate our approach on a number of benchmarks, and experimental results justify the effectiveness and scalability of our proposed approach.

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AAAI Conference 2022 Conference Paper

Knowledge Compilation Meets Logical Separability

Junming Qiu
Wenqing Li
Zhanhao Xiao
Quanlong Guan
Liangda Fang
Zhao-Rong Lai
Qian Dong

Knowledge compilation is an alternative solution to address demanding reasoning tasks with high complexity via converting knowledge bases into a suitable target language. Interestingly, the notion of logical separability, proposed by Levesque, offers a general explanation for the tractability of clausal entailment for two remarkable languages: decomposable negation normal form and prime implicates. It is interesting to explore what role logical separability on earth plays in problem tractability. In this paper, we apply the notion of logical separability in three reasoning problems within the context of propositional logic: satisfiability check (CO), clausal entailment check (CE) and model counting (CT), contributing to three corresponding polytime procedures. We provide three logical separability based properties: CO-logical separability, CE-logical separability and CT-logical separability. We then identify three novel normal forms: CO-LSNNF, CE-LSNNF and CT-LSNNF based on the above properties. Besides, we show that every normal form is the necessary and sufficient condition under which the corresponding procedure is correct. We finally integrate the above four normal forms into the knowledge compilation map.

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