Ji Wang

JBHI Journal 2026 Journal Article

Adaptive Spectral Graph Attention Filtering Network for Alzheimer's Disease Classification Using Multimodal Data

• Zhi Yang
• Bo Cheng
• Haitao Gan
• Zhongwei Huang
• Ran Zhou
• Ji Wang

Early detection of Alzheimer's Disease (AD) is critical for timely intervention and management. However, existing graph-based approaches often fail to fully leverage the rich spectral-domain information inherent in brain network signals. To address this limitation, we propose an Adaptive Spectral Graph Attention Filtering Network (ASGAFN), which effectively models the spectral structures of functional and structural brain networks to enhance classification performance. Specifically, we first construct structural and functional brain network graphs from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI). Subsequently, a frequency-encoding-guided attention mechanism is designed to learn a shared spectral response function across graphs. This enables the construction of interpretable and adaptive spectral filters while mitigating semantic misalignment across different spectral domains. Furthermore, a spectral energy sensing module is incorporated to facilitate graph-specific adaptation, thereby enhancing flexibility and subject-level discriminability. Finally, the refined spectral signals are transformed back to the spatial domain and fused via a Multimodal Fusion and Enhancement Layer (MFEL). Extensive experiments demonstrate that ASGAFN significantly outperforms multiple baselines in AD-related classification tasks. It achieves accuracies of 96. 64% (AD vs. NC), 90. 48% (MCI vs. NC), and 91. 75% (AD vs. MCI). Additionally, it attains an accuracy of 87. 12% in the three-class classification task, underscoring its effectiveness in distinguishing among multiple disease stages. These findings validate the efficacy of spectral-domain modeling and multimodal fusion.

FM Conference 2026 Conference Paper

EUF-based Solving Dyck-Reachability with Applications to Static Analysis

• Yide Du
• Zhenbang Chen
• Kunlin Liu
• Guofeng Zhang
• Xudong Wang
• Ke Ma
• Wei Dong
• Ji Wang

Abstract Static analysis plays a crucial role in program optimization, bug detection, and automated testing. Dyck-reachability provides a foundational formulation for static analysis, as Dyck grammars can model critical properties such as field and context sensitivity, thus offering broad applicability. This paper shows that static analysis problems modeled as Dyck-reachability on bidirected graphs can be encoded into the EUF SMT theory; consequently, all such problems admit efficient formulation and solution via EUF-based SMT solvers. By leveraging the optimized nature of modern SMT solvers, our method achieves efficiency comparable to state-of-the-art graph-based bidirected Dyck-reachability algorithms while eliminating the need for developing complex specialized graph reachability algorithms. Our approach opens new avenues for solving these classical static analysis problems, demonstrating the strong potential of SMT solvers in encoding static analysis solutions.

AAAI Conference 2026 Conference Paper

PurMM: Attention-Guided Test-Time Backdoor Purification in Multimodal Large Language Models

• Wenzheng Jiang
• Ke Liang
• Xuankun Rong
• Jingxuan Zhou
• Zhengyi Zhong
• Guancheng Wan
• Ji Wang

Downstream fine-tuning of Multimodal Large Language Models (MLLMs) is advancing rapidly, allowing general models to achieve superior performance on domain-specific tasks. Yet most prior research focuses on performance gains and overlooks the vulnerability of the fine-tuning pipeline: attackers can easily poison the dataset to implant backdoors into MLLMs. We conduct an in-depth investigation of backdoor attacks on MLLMs and reveal the phenomenon of Attention Hijacking and its Hierarchical Mechanism. Guided by this insight, we propose PurMM, a test-time backdoor purification framework that removes visual tokens exhibiting anomalous attention, thereby avoiding targeted outputs while restoring correct answers. PurMM contains three stages: (1) locating tokens with abnormal attention, (2) filtering them using deep-layer cues, and (3) zeroing out their corresponding components in the visual embeddings. Unlike existing defences, PurMM dispenses with retraining and training-process modifications, operating at test-time to restore model performance while eliminating the backdoor. Extensive experiments across multiple MLLMs and datasets show that PurMM maintains normal performance, sharply reduces attack success rates, and consistently converts backdoor outputs to benign ones, offering a new perspective for safeguarding MLLMs.

AAMAS Conference 2026 Conference Paper

Reliable Data Science Analysis with Large Language Models via Multi-Agent Tools Orchestration

• Mingyin Zou
• Guangrong You
• Xiaomin Zhu
• Yanqing Ye
• Ji Wang

WhileLargeLanguageModels(LLMs)showpromiseforautomating the labor-intensive process of data science analysis, their practical application is undermined by the generation of erroneous and unreliable code. We argue that this stems from treating LLMs as open-ended code generators—a task akin to answering an essay question. Weproposeafundamentalparadigmshift: ourframework reframes the task as one of structured tool selection and parameterization, effectively turning the “essay question” into a sequence of “multiple-choice and fill-in-the-blanks” problems. This shift dramatically reduces the potential for error. Our contributions are twofold. First, a multi-agent framework orchestrates the workflow, breaking down complex tasks into verifiable steps. Second, we construct an auto-generated tool library that supports a novel mechanism, empowering LLMs to select tools based on full source code rather than descriptions.

FM Conference 2026 Conference Paper

Selective Concolic Testing

• Guofeng Zhang
• Zhenbang Chen
• Ziqi Shuai
• Jun Sun
• Weijiang Hong
• Yufeng Zhang
• Ji Wang
• Yang Liu

Abstract The principled combination of symbolic execution and random testing lacks a formal foundation, especially in deciding which inputs to symbolize. We propose selective concolic testing, a cost-aware framework that formulates this choice as an optimized policy problem of a MDP (Markov Decision Process). We model program exploration over a finite control-flow graph, where MDP states represent covered statements, actions partition path constraints into symbolic and random fragments, rewards reflect coverage gain, and costs account for SMT solving effort and sampling inefficiency. Our framework yields the first formal characterization of selective symbolization as policy synthesis in a probabilistic system. We prove that exact policy computation is intractable due to the exponential state space and the hardness of solution-density estimation via model counting. Our formulation enables a practical approximation: we partition constraint dependency graphs and use machine learning to predict solver timeouts, guiding per-constraint symbolization decisions. Built on top of KLEE and JFS, our prototype validates the approach on real-world floating-point benchmarks. Results show that selectively symbolizing inputs, guided by predicted solvability and cost, significantly improves coverage efficiency. Our work thus provides both a rigorous theoretical foundation and a practical instantiation for hybrid program analysis.

IJCAI Conference 2025 Conference Paper

BTPG: A Platform and Benchmark for Behavior Tree Planning in Everyday Service Robots

• Xinglin Chen
• Yishuai Cai
• Minglong Li
• Yunxin Mao
• Zhou Yang
• Wenjing Yang
• Weixia Xu
• Ji Wang

Behavior Trees (BTs) are a widely used control architecture in robotics, renowned for their robustness and safety, which are especially crucial for everyday service robots. Recently, several methods have been proposed to automatically plan BTs to accomplish specific tasks. However, existing research in BT planning lacks two main aspects: (1) the absence of a standard platform for modeling and planning BTs, along with testing benchmarks; and (2) insufficient metrics for a comprehensive evaluation of BT planning algorithms. In this paper, we propose Behavior Tree Planning Gym (BTPG), the first platform and benchmark for BT planning in everyday service robots. In BTPG, behavior nodes are represented by predicate logic, and objects are categorized to better define the predicate domains and action models. The BT planning problem is then formulated in the STRIPS style. We support four environments and three simulators with different action models, which cover most of the needs of everyday service activities. We design a dataset generator for each environment and test three state-of-the-art BT planning algorithms, as well as one proposed by us, using various common metrics. In addition, we design three advanced metrics, planning progress, region distance, and execution robustness, to gain deeper insights into these BT planning algorithms. With a standard test benchmark, we hope BTPG can inspire and accelerate progress in the field of BT planning. Our codes are available at https: //github. com/DIDS-EI/BTPG.

AAMAS Conference 2025 Conference Paper

FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation

• Wenzheng Jiang
• Ji Wang
• Xiongtao Zhang
• Weidong Bao
• Cheston Tan
• Flint Xiaofeng Fan

This paper focuses on Federated Reinforcement Learning (FedRL) in black-box settings with heterogeneous agents. Existing studies mostly assume agent homogeneity and knowability of internal details. To tackle these issues, we propose Federated Heterogeneous Policy Distillation (FedHPD). FedHPD uses action probability distributions as a medium for knowledge sharing among heterogeneous agents. Extensive experiments show that FedHPD achieves significant improvements across various benchmark tasks.

NeurIPS Conference 2025 Conference Paper

Gains: Fine-grained Federated Domain Adaptation in Open Set

• Zhengyi Zhong
• Wenzheng Jiang
• Weidong Bao
• Ji Wang
• Qi Wang
• Guanbo Wang
• Yongheng Deng
• Ju Ren

Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios, introducing new knowledge. This raises two critical demands: detecting new knowledge, i. e. , knowledge discovery, and integrating it into the global model, i. e. , knowledge adaptation. Existing research focuses on coarse-grained knowledge discovery, and often sacrifices source domain performance and adaptation efficiency. To this end, we propose a fine-grained federated domain adaptation approach in open set (Gains). Gains splits the model into an encoder and a classifier, empirically revealing features extracted by the encoder are sensitive to domain shifts while classifier parameters are sensitive to class increments. Based on this, we develop fine-grained knowledge discovery and contribution-driven aggregation techniques to identify and incorporate new knowledge. Additionally, an anti-forgetting mechanism is designed to preserve source domain performance, ensuring balanced adaptation. Experimental results on multi-domain datasets across three typical data-shift scenarios demonstrate that Gains significantly outperforms other baselines in performance for both source-domain and target-domain clients. Code is available at: https: //github. com/Zhong-Zhengyi/Gains.

AAAI Conference 2025 Conference Paper

MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration

• Yishuai Cai
• Xinglin Chen
• Zhongxuan Cai
• Yunxin Mao
• Minglong Li
• Wenjing Yang
• Ji Wang

Multi-robot task planning and collaboration are critical challenges in robotics. While Behavior Trees (BTs) have been established as a popular control architecture and are plannable for a single robot, the development of effective multi-robot BT planning algorithms remains challenging due to the complexity of coordinating diverse action spaces. We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness. MRBTP features cross-tree expansion to coordinate heterogeneous actions across different BTs to achieve the team's goal. For homogeneous actions, we retain backup structures among BTs to ensure robustness and prevent redundant execution through intention sharing. While MRBTP is capable of generating BTs for both homogeneous and heterogeneous robot teams, its efficiency can be further improved. We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot. These relevant actions can be pre-planned to form long-horizon subtrees, significantly enhancing the planning speed and collaboration efficiency of MRBTP. We evaluate our algorithm in warehouse management and everyday service scenarios. Results demonstrate MRBTP's robustness and execution efficiency under varying settings, as well as the ability of the pre-trained LLM to generate effective task-specific subtrees for MRBTP.

EAAI Journal 2025 Journal Article

Sensitive components of temperature-induced track deformation on cable-stayed bridge impacting dynamic response of high-speed train based on deep learning

• Qian Zhang
• Xiaopei Cai
• Jijun Wang
• Weibin Liu
• Yilin Zhong
• Moyan Zhang
• Ji Wang

To explore the impact of different components of track deformation caused by temperature variations of cable-stayed bridges (CSBs) and slab tracks on the dynamic response of high-speed trains (HSTs), this paper established an analytical framework based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and deep learning. Initially, a finite element model for the slab track-CSB and a multi-body dynamics model for HST-CSB were established to calculate the temperature-induced deformation of the slab tracks on the CSB and the corresponding dynamic response of the HST. Subsequently, the ICEEMDAN and Convolutional Neural Networks (CNN)-Bi-Long Short-term Memory (BiLSTM)-Convolutional Block Attention Module (CBAM), were fused to obtain the integrated ICEEMDAN-CNN-BiLSTM-CBAM (ICBC) model. The ICEEMDAN can effectively decompose the signal of track deformation by frequency, while BiLSTM had superior performance in capturing the spatial features of input data. The model could establish the mapping relationship between track deformation components and vehicle dynamic responses. The main conclusions obtained are as follows: When both the CSB and slab track experienced a simultaneous temperature rise, it resulted in an intricate combination of short-wave track deformations, long-wave bridge deformations, and localized deformations induced by girder ends. At the mid-span location, since the span length is as large as 450m, the wavelength of the deformation induced by temperature variations has also increased. By extracting the weight vectors of the output neural network model, the contribution of each Intrinsic Mode Functions to the vehicle dynamic response was obtained. The dynamic response of HSTs was most sensitive to the long-wave and large-amplitude components in the temperature deformation of slab track on CSB. This study could contribute to the understanding of the spatial deformation components of slab tracks on CSB and provide scientific predictions for the dynamic response of HSTs.

IJCAI Conference 2024 Conference Paper

Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions

• Xinglin Chen
• Yishuai Cai
• Yunxin Mao
• Minglong Li
• Wenjing Yang
• Weixia Xu
• Ji Wang

Robots executing tasks following human instructions in domestic or industrial environments essentially require both adaptability and reliability. Behavior Tree (BT) emerges as an appropriate control architecture for these scenarios due to its modularity and reactivity. Existing BT generation methods, however, either do not involve interpreting natural language or cannot theoretically guarantee the BTs' success. This paper proposes a two-stage framework for BT generation, which first employs large language models (LLMs) to interpret goals from high-level instructions, then constructs an efficient goal-specific BT through the Optimal Behavior Tree Expansion Algorithm (OBTEA). We represent goals as well-formed formulas in first-order logic, effectively bridging intent understanding and optimal behavior planning. Experiments in the service robot validate the proficiency of LLMs in producing grammatically correct and accurately interpreted goals, demonstrate OBTEA's superiority over the baseline BT Expansion algorithm in various metrics, and finally confirm the practical deployability of our framework. The project website is https: //dids-ei. github. io/Project/LLM-OBTEA.

AAAI Conference 2024 Conference Paper

Scaling Few-Shot Learning for the Open World

• Zhipeng Lin
• Wenjing Yang
• Haotian Wang
• Haoang Chi
• Long Lan
• Ji Wang

Few-shot learning (FSL) aims to enable learning models with the ability to automatically adapt to novel (unseen) domains in open-world scenarios. Nonetheless, there exists a significant disparity between the vast number of new concepts encountered in the open world and the restricted available scale of existing FSL works, which primarily focus on a limited number of novel classes. Such a gap hinders the practical applicability of FSL in realistic scenarios. To bridge this gap, we propose a new problem named Few-Shot Learning with Many Novel Classes (FSL-MNC) by substantially enlarging the number of novel classes, exceeding the count in the traditional FSL setup by over 500-fold. This new problem exhibits two major challenges, including the increased computation overhead during meta-training and the degraded classification performance by the large number of classes during meta-testing. To overcome these challenges, we propose a Simple Hierarchy Pipeline (SHA-Pipeline). Due to the inefficiency of traditional protocols of EML, we re-design a lightweight training strategy to reduce the overhead brought by much more novel classes. To capture discriminative semantics across numerous novel classes, we effectively reconstruct and leverage the class hierarchy information during meta-testing. Experiments show that the proposed SHA-Pipeline significantly outperforms not only the ProtoNet baseline but also the state-of-the-art alternatives across different numbers of novel classes.

TIST Journal 2022 Journal Article

FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device

• Zhengyi Zhong
• Weidong Bao
• Ji Wang
• Xiaomin Zhu
• Xiongtao Zhang

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework FLEE, which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by FLEE consume less global computing resource in the inference stage.

AAAI Conference 2021 Conference Paper

Dec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination

• Minglong Li
• Zhongxuan Cai
• Wenjing Yang
• Lixia Wu
• Yinghui Xu
• Ji Wang

Multi-agent coordination tends to benefit from efficient communication, where cooperation often happens based on exchanging information about what the agents intend to do, i. e. intention sharing. It becomes a key problem to model the intention by some proper abstraction. Currently, it is either too coarse such as final goals or too fined as primitive steps, which is inefficient due to the lack of modularity and semantics. In this paper, we design a novel multi-agent coordination protocol based on subgoal intentions, defined as the probability distribution over feasible subgoal sequences. The subgoal intentions encode macro-action behaviors with modularity so as to facilitate joint decision making at higher abstraction. Built over the proposed protocol, we present Dec-SGTS (Decentralized Sub-Goal Tree Search) to solve decentralized online multi-agent planning hierarchically and efficiently. Each agent runs Dec-SGTS asynchronously by iteratively performing three phases including local sub-goal tree search, local subgoal intention update and global subgoal intention sharing. We conduct the experiments on courier dispatching problem, and the results show that Dec-SGTS achieves much better reward while enjoying a significant reduction of planning time and communication cost compared with Dec-MCTS (Decentralized Monte Carlo Tree Search).

IJCAI Conference 2020 Conference Paper

Learning with Noise: Improving Distantly-Supervised Fine-grained Entity Typing via Automatic Relabeling

• Haoyu Zhang
• Dingkun Long
• Guangwei Xu
• Muhua Zhu
• Pengjun Xie
• Fei Huang
• Ji Wang

Fine-grained entity typing (FET) is a fundamental task for various entity-leveraging applications. Although great success has been made, existing systems still have challenges in handling noisy samples in training data introduced by distant supervision methods. To address these noise, previous studies either focus on processing the clean samples (i, e. , have only one label) and noisy samples (i, e. , have multiple labels) with different strategies or filtering the noisy labels based on the assumption that the distantly-supervised label set certainly contains the correct type label. In this paper, we propose a probabilistic automatic relabeling method which treats all training samples uniformly. Our method aims to estimate the pseudo-truth label distribution of each sample, and the pseudo-truth distribution will be treated as part of trainable parameters which are jointly updated during the training process. The proposed approach does not rely on any prerequisite or extra supervision, making it effective on real applications. Experiments on several benchmarks show that our method outperforms previous approaches and alleviates the noisy labeling problem.

IJCAI Conference 2019 Conference Paper

Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty

• Minglong Li
• Wenjing Yang
• Zhongxuan Cai
• Shaowu Yang
• Ji Wang

The performance of decentralized multi-agent systems tends to benefit from information sharing and its effective utilization. However, too much or unnecessary sharing may hinder the performance due to the delay, instability and additional overhead of communications. Aiming to a satisfiable coordination performance, one would prefer the cost of communications as less as possible. In this paper, we propose an approach for improving the sharing utilization by integrating information sharing with prediction in decentralized planning. We present a novel planning algorithm by combining decision sharing and prediction based on decentralized Monte Carlo Tree Search called Dec-MCTS-SP. Each agent grows a search tree guided by the rewards calculated by the joint actions, which can not only be sampled from the shared probability distributions over action sequences, but also be predicted by a sufficiently-accurate and computationally-cheap heuristics-based method. Besides, several policies including sparse and discounted UCT and DIY-bonus are leveraged for performance improvement. We have implemented Dec-MCTS-SP in the case study on multi-agent information gathering under threat and uncertainty, which is formulated as Decentralized Partially Observable Markov Decision Process (Dec-POMDP). The factored belief vectors are integrated into Dec-MCTS-SP to handle the uncertainty. Comparing with the random, auction-based algorithm and Dec-MCTS, the evaluation shows that Dec-MCTS-SP can reduce communication cost significantly while still achieving a surprisingly higher coordination performance.

AAAI Conference 2019 Conference Paper

Private Model Compression via Knowledge Distillation

• Ji Wang
• Weidong Bao
• Lichao Sun
• Xiaomin Zhu
• Bokai Cao
• Philip S. Yu

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices’ capacity. What is worse, app service providers need to collect and utilize a large volume of users’ data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9. 83, 10−6 )-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0. 97% accuracy loss.

IJCAI Conference 2015 Conference Paper

A Simple Probabilistic Extension of Modal Mu-calculus

• Wanwei Liu
• Lei Song
• Ji Wang
• Lijun Zhang

Probabilistic systems are an important theme in AI domain. As the specification language, PCTL is the most frequently used logic for reasoning about probabilistic properties. In this paper, we present a natural and succinct probabilistic extension of µcalculus, another prominent logic in the concurrency theory. We study the relationship with PCTL. Surprisingly, the expressiveness is highly orthogonal with PCTL. The proposed logic captures some useful properties which cannot be expressed in PCTL. We investigate the model checking and satisfiability problem, and show that the model checking problem is in UP ∩co-UP, and the satisfiability checking can be decided via reducing into solving parity games. This is in contrast to PCTL as well, whose satisfiability checking is still an open problem.

TCS Journal 2012 Journal Article

Failure-divergence semantics and refinement of long running transactions

• Zhenbang Chen
• Zhiming Liu
• Ji Wang

Compensating CSP (cCSP) models long-running transactions. It can be used to specify service orchestrations written in programming languages like WS-BPEL. However, the original cCSP does not allow to model internal (non-deterministic) choice, synchronized parallel composition, hiding or recursion. In this paper, we introduce these operators and define for the extended language a failure-divergence (FD) semantics to allow reasoning about non-determinism, deadlock and livelock. Furthermore, we develop a refinement calculus that allows us to compare the level of non-determinism between long running transactions, and transform specifications for design and analysis.

IJCAI Conference 2011 Conference Paper

A Wikipedia Based Semantic Graph Model for Topic Tracking in Blogsphere

• Jintao Tang
• Ting Wang
• Qin Lu
• Ji Wang
• Wenjie Li

There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of-mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the named entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through a graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested using real-world blog data. Experimental results show the advantage of the proposed method on tracking topics in short, noisy text.

AAMAS Conference 2005 Conference Paper

Towards An Agent Oriented Programming Language with Caste and Scenario Mechanisms

• Ji Wang
• Rui Shen
• Hong Zhu