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Hybrid knowledge reasoning over knowledge hypergraph:Inductive,deductive,and abductive 认领 引用
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作者 Ling Tian Lei Gao +3 位作者 Ben Zhang Xiao Liu Yi-Nong Shi Hui Gao 《Journal of Electronic Science and Technology》 EI CAS CSCD 2026年第2期11-26,共16页
Traditional knowledge reasoning methods,which are predominantly reliant on static rules and structured data,often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios.To overcome these limi... Traditional knowledge reasoning methods,which are predominantly reliant on static rules and structured data,often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios.To overcome these limitations,this study proposes a novel reasoning framework based on a three-layered knowledge hypergraph.Core innovation lies in the synergy of inductive,deductive,and abductive reasoning mechanisms to enhance both reliability and interpretability.Specifically,hypergraph-based inductive reasoning extracts robust evolutionary patterns by mining the historical subgraph structures.Deductive reasoning ensures transparency by constructing tree-shaped inference paths,whereas abductive reasoning establishes causal traceability by forming evidence chains from historical contexts.Experimental evaluations on the Integrated Crisis Early Warning System(ICEWS)dataset demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy and interpretability,thereby offering a scalable solution for complex event analysis. 展开更多
关键词 Abductive reasoning Deductive reasoning Inductive reasoning Knowledge hypergraphs Knowledge reasoning
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A Comprehensive Review of Complex Logical Reasoning in Large Vision-Language Models 认领 引用
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作者 Weiqiang Jin Yang Liu +9 位作者 Yang Gao Shixiang Tang Yanghao Zhou Jinhu Qi Wentao Zhang Junli Wang Jing Gao Yue Ma Ziwei Zhang Biao Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1-51,共51页
relational reasoning,and cross-modal evidence integration.Reasoning abilities such as deductive,inductive,abductive,multi-hop,and causal inference are fundamental to robust decision making,trustworthy interaction,and ... relational reasoning,and cross-modal evidence integration.Reasoning abilities such as deductive,inductive,abductive,multi-hop,and causal inference are fundamental to robust decision making,trustworthy interaction,and real-world deployment,yet they have not been systematically examined in the LVLM literature.Existing surveys mainly discuss mathematical reasoning,general multimodal intelligence,or benchmark progress,but they do not provide a unified account of complex logical reasoning in LVLMs,including its definition,reasoning types,modeling paradigms,evaluation protocols,and unresolved limitations.To address this gap,this survey develops a unified analytical framework for complex logical reasoning in LVLMs.This survey provides a structured review of this emerging area.We first formalize complex logical reasoning in multimodal settings and organize the literature into five recurrent reasoning families:deductive,inductive,abductive,multi-hop,and causal reasoning.We then review reasoning-oriented LVLM architectures,including unified,modular,and tool-augmented paradigms,and summarize major reasoning mechanisms such as chain-of-thought,program-based reasoning,self-correction,and interpretability-oriented analysis.We further examine representative benchmarks and evaluation protocols,with particular attention to the mismatch between final-answer accuracy and genuine reasoning validity.Based on empirical evidence from representative LVLMs and datasets,we identify common capability trends,recurring failure modes,and key open challenges.Our analysis shows that current LVLMs still struggle with reasoning faithfulness,long-horizon inference,cross-modal grounding,hallucination control,and process-aware evaluation.Finally,we outline future directions in reasoning-oriented data construction,model design,training strategies,evaluation methodology,and deployment.Overall,this survey offers a unified conceptual framework and technical roadmap for advancing LVLMs from strong perceptual systems toward reliable multimodal reasoning agents. 展开更多
关键词 **:Large vision-language model complex logical reasoning multimodal reasoning chain-of-thought evaluation benchmark reasoning faithfulness
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CellReasoner:a reasoning-enhanced large language model for cell type annotation 认领 引用
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作者 Guangshuo Cao Yi Shen +4 位作者 Lingyu Zhao Jianghong Wu Haoyu Chao Ming Chen Dijun Chen 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第4期762-765,共4页
Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,... Cell type annotation is a fundamental step in single-cell data analysis,and it also represents a reasoning process that integrates diverse sources of evidence,including gene expression profiles,canonical marker genes,and reference datasets,to accurately infer cellular identities.Similar to stepwise inference in artificial intelligence,this process relies on combining prior knowledge with context-specific features to achieve confident classification.Recent advances in large language models have shown that sufficiently scaled models can perform sophisticated reasoning across mathematical,logical,and programming tasks(Azerbayev et al.,2023;Jaech et al.,2024;Guo et al.,2025;Ye et al.,2025).This progress highlights the potential for leveraging LLM-based reasoning paradigms to enhance complex biological inference tasks such as automated cell type annotation. 展开更多
关键词 combining prior knowledge gene expression profilescanonical cell type annotation artificial intelligencethis reasoning large language models stepwise inference reasoning process
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MultiAgent-CoT:A Multi-Agent Chain-of-Thought Reasoning Model for Robust Multimodal Dialogue Understanding 认领 引用
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作者 Ans D.Alghamdi 《Computers, Materials & Continua》 SCIE EI 2026年第2期1395-1429,共35页
Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ... Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches. 展开更多
关键词 Multi-agent systems chain-of-thought reasoning multimodal dialogue conversational artificial intelligence(AI) cross-modal fusion reasoning Interpretability
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Agentic AI:The age of reasoning——A review 认领 引用 被引量：2
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作者 Ume Nisa Muhammad Shirazi +1 位作者 Mohamed Ali Saip Muhammad Syafiq Mohd Pozi 《Journal of Automation and Intelligence》 EI CSCD 2026年第1期69-89,共21页
Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independent... Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems. 展开更多
关键词 Agentic AI Autonomous systems Artificial intelligence Large language models(LLMs) Reasoning agents AI taxonomy
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Monitoring of Drill-and-Blast Workflows at the Tunnel Face Using Computer Vision and Context Reasoning 认领 引用
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作者 Chuanjiang Chen Junyong Zhou +3 位作者 Binbin Du Miaosi Dong Liwen Zhang Bitang Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期560-585,共26页
Computer vision has been widely adopted in intelligent construction monitoring;however,existing studies primarily focus on identifying individual construction elements or isolated activities,with limited capability fo... Computer vision has been widely adopted in intelligent construction monitoring;however,existing studies primarily focus on identifying individual construction elements or isolated activities,with limited capability for integrated monitoring of complete construction workflows.Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites.To address the challenge of reliable visual recognition in drill-andblast tunnel environments characterized by uneven illumination,localized glare,and dust interference,this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning.The framework consists of three components:(1)a construction workflow model with a sequence library database,(2)a robust construction element recognition model combining an enhanced YOLOvll with Segment Anything Model 2(SAM2),and(3)a hierarchical workflow reasoning mechanism driven by domain knowledge.A hierarchical workflow model embedding procedural logic is established through field investigation and normative analysis.SAM2 is employed for automated dataset annotation,while YOLOvll is structurally enhanced with Convolutional Block Attention Module(CBAM),Adaptive Feature Enhancement(AFE),and Swin Transformer modules to improve feature representation and adaptability to degraded visual conditions.Workflow identification is finally achieved by integrating visual perception outputs with hierarchical context reasoning.Validation in an active drill-and-blast tunnel shows that the proposed method attains an average detection precision of 91.1%across 1l construction element categories,exceeding 95%for large equipment,and an average workflow recognition accuracy of 94%.The results demonstrate the effectiveness of the proposed framework for monitoring the tunnel construction workflow and supporting construction management. 展开更多
关键词 Drill-and-blast tunnel construction workflow intelligent construction monitoring computer vision workflow recognition context reasoning
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Resolving Ambiguity in Pointing Gestures Using Contextual Reasoning from Large Language Models 认领 引用
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作者 Sumin Yeon Minjae Lee +1 位作者 Jiho Bae Suwon Lee 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1010-1024,共15页
In everyday life,people effectively convey their intentions through pointing gestures without explicitly naming objects.In particular,pointing gestures used in conjunction with linguistic expressions such as“this”an... In everyday life,people effectively convey their intentions through pointing gestures without explicitly naming objects.In particular,pointing gestures used in conjunction with linguistic expressions such as“this”and“that”play a crucial role in intuitively indicating objects or locations in space.Although research on the recognition of such nonverbal gestures has been actively pursued within the field of human-computer interaction(HCI),accurately interpreting a user’s intent remains challenging in situations where the pointing gesture is ambiguous.This paper proposes an integrated system that combines a large language model(LLM),capable of understanding complex human language expressions,with pointing gestures designed to designate targets in space,thereby effectively processing multimodal user commands.The system is designed to accurately recognize user intentions even in complex and uncertain environments(e.g.,indoor spaces with multiple objects)by synergistically leveraging spatial information obtained from pointing gestures and contextual reasoning provided by the LLM.To validate the proposed approach,we constructed a dataset comprising complex real-world environments and diverse utterances,and conducted experiments to meticulously analyze the system’s performance and limitations.This study demonstrates the potential for natural expansion of language-based spatial understanding within HCI,and suggests avenues for future research in related fields. 展开更多
关键词 Multimodal interaction pointing gesture large language model contextual reasoning object referencing human-computer interaction
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Can Causal Circuits Enable Efficient Spatial Reasoning for Geoinformatics in Large Language Models? 认领 引用
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作者 Sahil Tripathi Manaswi Kulahara +1 位作者 Abdul Khader Jilani Saudagar Hatoon S.AlSagri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1072-1095,共24页
Spatial reasoning,defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding.However,existing works such as Bidir... Spatial reasoning,defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding.However,existing works such as Bidirectional Encoder Representations from Transformers(BERT)-based spatial Question Answering(QA)models and neuro-symbolic models rely on dataset-specific patterns,leading to shortcut learning,where reliance on superficial lexical cues rather than true relational understanding.Recent Large Language Models(LLMs)-based works,including fine-tuning and Chain-of-Thought(CoT)prompting,partially alleviate shortcut learning but remain limited by non-causal reasoning,where predictions depend on spurious correlations rather than stable relational structure.To address these limitations,we propose Causal Inference and Reasoning via Compact sUbnetwork IdenTification(CIRCUIT-X),motivated by the hypothesis that spatial reasoning in LLMs is governed by compact causal parameter subsets(a.k.a causal circuits).CIRCUIT-X operates in two stages:(i)Causal Importance Estimation(Stage Ⅰ)via structured interventions to mitigate shortcut learning,and(ii)Minimal Circuit Discovery(Stage Ⅱ)via structured pruning to mitigate non-causal reasoning.Empirically,CIRCUIT-X achieves up to 91%accuracy on SPAtial Reasoning on Textual Question Answering(SPARTQA)and 87%on StepGame,outperforming State-of-the-Art(SOTA)methods while improving intervention robustness by up to+11%and causal consistency by up to+16%.Therefore,it retains up to 96%of full-model performance using only 3%–6%of active parameters,while demonstrating strong robustness under cross-domain transfer with improvements of up to+10%in accuracy and substantially higher intervention stability under distribution shifts. 展开更多
关键词 Causal circuits geoinformatics large language models parameter-efficient learning spatial reasoning structured pruning
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Dynamic Knowledge Graph Reasoning Based on Distributed Representation Learning 认领 引用
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作者 Qiuru Fu Shumao Zhang +4 位作者 Shuang Zhou Jie Xu Changming Zhao Shanchao Li Du Xu 《Computers, Materials & Continua》 SCIE EI 2026年第2期1542-1560,共19页
Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowled... Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance. 展开更多
关键词 Dynamic knowledge graph reasoning recurrent neural network graph convolutional network graph attention mechanism
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Cascading Class Activation Mapping:A Counterfactual Reasoning-Based Explainable Method for Comprehensive Feature Discovery 认领 引用
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作者 Seoyeon Choi Hayoung Kim Guebin Choi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1043-1069,共27页
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati... Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods. 展开更多
关键词 Explainable AI class activation mapping counterfactual reasoning shortcut learning feature discovery
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Cognitive signatures of conditional reasoning dysfunction in major depression 认领 引用
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作者 Elif Yöyen 《World Journal of Psychiatry》 SCIE 2026年第2期414-418,共5页
Major depressive disorder(MDD)represents one of the most urgent global mental health challenges,affecting hundreds of millions of individuals across cultures and socioeconomic contexts.While the affective and motivati... Major depressive disorder(MDD)represents one of the most urgent global mental health challenges,affecting hundreds of millions of individuals across cultures and socioeconomic contexts.While the affective and motivational dimensions of depression have long been emphasized,the cognitive dimension of the disorder has increasingly attracted attention.Within this cognitive framework,the study by Li et al represents an important milestone.It is the first investigation to combine the Wason selection task(WST),a classical paradigm for examining conditional reasoning,with event-related potentials(ERP),a method uniquely suited for revealing the temporal dynamics of cognitive processing.By integrating behavioral performance with electrophysiological measures,the authors provide valuable new insights into the neural mechanisms underlying reasoning dysfunction in MDD.However,while this study makes an important contribution,caution is warranted in interpreting its clinical and diagnostic implications.The methodological limitations,such as small sample size,limited ecological validity of the WST,and absence of control for confounding variables,should be carefully considered when evaluating the generalizability of ERP findings.Beyond summarizing the findings of Li et al,this letter emphasizes both the strengths and weaknesses of their approach.While the integration of cognitive reasoning and neurophysiological evidence is commendable,the lack of replication and comparative data leaves important open questions about how these results align with prior ERP or functional magnetic resonance imaging studies of depressive cognition.A more critical synthesis of these contextual gaps enhances the interpretative depth of the article.Overall,the study offers valuable preliminary evidence of conditional reasoning dysfunction in MDD,but its conclusions should be viewed as exploratory rather than definitive.Future research must address methodological limitations before clinical translation is possible. 展开更多
关键词 Major depression Neurological basis Wason selection task Conditional reasoning Cognitive neuroscience
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Functional evidential reasoning model(FERM)-A new systematic approach for exploring hazardous chemical operational accidents under uncertainty 认领 引用 被引量：1
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作者 Qianlin Wang Jiaqi Han +6 位作者 Lei Cheng Feng Wang Yiming Chen Zhan Dou Bing Zhang Feng Chen Guoan Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期255-269,共15页
This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal... This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective. 展开更多
关键词 Functional evidential reasoning model (FERM) Accident causation analysis Operational accidents Hazardous chemical Uncertainty
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A Novel Multi-Modal Neurosymbolic Reasoning Intelligent Algorithm for BLMP Equation 认领 引用 被引量：1
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作者 Hanwen Zhang Runfa Zhang Qirang Liu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第10期13-17,共5页
The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensiona... The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensional space and one-dimensional time.With broad applications spanning fluid dynamics,shallow water waves,plasma physics,and condensed matter physics,the investigation of its solutions holds significant importance.Traditional analytical methods face limitations due to their dependence on bilinear forms.To overcome this constraint,this letter proposes a novel multi-modal neurosymbolic reasoning intelligent algorithm(MMNRIA)that achieves 100%accurate solutions for nonlinear partial differential equations without requiring bilinear transformations.By synergistically integrating neural networks with symbolic computation,this approach establishes a new paradigm for universal analytical solutions of nonlinear partial differential equations.As a practical demonstration,we successfully derive several exact analytical solutions for the(3+1)-dimensional BLMP equation using MMNRIA.These solutions provide a powerful theoretical framework for studying intricate wave phenomena governed by nonlinearity and dispersion effects in three-dimensional physical space. 展开更多
关键词 intelligent algorithm dimensional Boiti Leon Manna Pempinelli equation fluid dynamicsshallow water wavesplasma physicsand nonlinear evolution equation condensed matter physicsthe neurosymbolic reasoning characterizing complex nonlinear dynamic phenomena analytical methods
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Extrapolation Reasoning on Temporal Knowledge Graphs via Temporal Dependencies Learning 认领 引用
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作者 Ye Wang Binxing Fang +3 位作者 Shuxian Huang Kai Chen Yan Jia Aiping Li 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第3期815-826,共12页
Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,ca... Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model. 展开更多
关键词 extrapolation link prediction temporal knowledge graph reasoning
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A Novel Evidential Reasoning Rule with Causal Relationships between Evidence 认领 引用
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作者 Shanshan Liu Liang Chang +1 位作者 Guanyu Hu Shiyu Li 《Computers, Materials & Continua》 SCIE EI 2025年第10期1113-1134,共22页
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio... The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation. 展开更多
关键词 Evidential Reasoning Rule uncertainty causal strength causal relationship transfer entropy complex system evaluation
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Neural correlates of conditional reasoning dysfunction in major depression:An event-related potential study with the Wason selection task 认领 引用 被引量：1
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作者 Jia-Xv Li Mei-Chen Lu +7 位作者 Luo-An Wu Wei Li Yu Li Xin-Ping Li Xiao-Hong Liu Xue-Zheng Gao Zhen-He Zhou Hong-Liang Zhou 《World Journal of Psychiatry》 SCIE 2025年第12期107-119,共13页
BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To inv... BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients. 展开更多
关键词 Major depression Event-related potential Wason selection task Conditional reasoning Neural mechanism
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Case-based reasoning of operation strategies recommendation for UAV swarm 认领 引用 被引量：1
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作者 HUANG Meigen WANG Tao +3 位作者 JING Tian YANG Song ZHOU Xin HE Hua 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第6期1548-1561,共14页
Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge a... Aiming at the characteristics of autonomy,confrontation,and uncertainty in unmanned aerial vehicle(UAV)swarm operations,case-based reasoning(CBR)technology with advantages such as weak dependence on domain knowledge and efficient problem-solving is introduced,and a recommendation method for UAV swarm operation strategies based on CBR is proposed.Firstly,we design a universal framework for UAV swarm operation strategies from three dimensions:operation effectiveness,time,and cost.Secondly,based on the representation of operation cases,certain,fuzzy,interval,and classification attribute similarity calculation methods,as well as entropybased attribute weight allocation methods,are suggested to support the calculation of global similarity of cases.This method is utilized to match the source case with the most similarity from the historical case library,to obtain the optimal recommendation strategy for the target case.Finally,in the form of red blue confrontation,a UAV swarm operation strategy recommendation case is constructed based on actual battle cases,and a system simulation analysis is conducted.The results show that the strategy given in the example performs the best in three evaluation indicators,including cost-effectiveness,and overall outperforms other operation strategies.Therefore,the proposed method has advantages such as high real-time performance and interpretability,and can address the issue of recommending UAV swarm operation strategies in complex battlefield environments across both online and offline modes.At the same time,this study could also provide new ideas for the selection of UAV swarm operation strategies. 展开更多
关键词 case-based reasoning(CBR) unmanned aerial vehicle(UAV)swarm operation strategy mixed retrieval
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Visible-Infrared Person Re-Identification via Quadratic Graph Matching and Block Reasoning 认领 引用
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作者 Junfeng Lin Jialin Ma +3 位作者 Wei Chen Hao Wang Weiguo Ding Mingyao Tang 《Computers, Materials & Continua》 SCIE EI 2025年第7期1013-1029,共17页
The cross-modal person re-identification task aims to match visible and infrared images of the same individual.The main challenges in this field arise from significant modality differences between individuals and the ... The cross-modal person re-identification task aims to match visible and infrared images of the same individual.The main challenges in this field arise from significant modality differences between individuals and the lack of high-quality cross-modal correspondence methods.Existing approaches often attempt to establish modality correspondence by extracting shared features across different modalities.However,these methods tend to focus on local information extraction and fail to fully leverage the global identity information in the cross-modal features,resulting in limited correspondence accuracy and suboptimal matching performance.To address this issue,we propose a quadratic graph matching method designed to overcome the challenges posed by modality differences through precise cross-modal relationship alignment.This method transforms the cross-modal correspondence problem into a graph matching task and minimizes the matching cost using a center search mechanism.Building on this approach,we further design a block reasoning module to uncover latent relationships between person identities and optimize the modality correspondence results.The block strategy not only improves the efficiency of updating gallery images but also enhances matching accuracy while reducing computational load.Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods on the SYSU-MM01,RegDB,and RGBNT201 datasets,achieving excellent matching accuracy and robustness,thereby validating its effectiveness in cross-modal person re-identification. 展开更多
关键词 Cross-modal person re-identification modal correspondence quadratic graph matching block reasoning
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Parental cognitive ability effects on children’s logical reasoning ability:The mediating role of academic expectation and the family environment 认领 引用
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作者 Qing Wang Haiyan Xu Xuhuan Wang 《Journal of Psychology in Africa》 2025年第4期497-503,共7页
This study investigated the relationship between parental cognitive ability and child logical reasoning ability,and the role of academic expectation and family environment in that relationship.Based on the 2020 China ... This study investigated the relationship between parental cognitive ability and child logical reasoning ability,and the role of academic expectation and family environment in that relationship.Based on the 2020 China Family Panel Studies(CFPS)data,1491 children(girls ratio=53.78%;average grade=6.023 years,school grade standard deviation=1.825 years).Results following multiple regression model(OLS)show that the higher the parental cognitive ability,the higher the children’s logical reasoning ability.Secondly,parental academic expectation serves as a mediator between their cognitive ability and children’s logical reasoning ability for higher logical reasoning by children.Third,a possible family environment acts as a mediator in the relationship between parents’cognitive ability and children’s logical reasoning ability to be higher.We conclude from thesefindings that parents with high cognitive abilities can enhance their children’s logical reasoning skills not only by setting higher academic expectations,but also by cultivating a supportive family environment.Thesefindings imply a need for intervention to improve family quality of life to enhance children’s thinking abilities to optimize their academic learning. 展开更多
关键词 parental cognitive ability children’s logical reasoning ability academic expectation family environment intermediary role
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Select-and-Answer Prompting:Facilitating LLMs for Improving Zero-Shot Reasoning 认领 引用
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作者 WANG Yufang TANG Xuesong HAO Kuangrong 《Journal of Donghua University(English Edition)》 CAS 2025年第5期513-522,共10页
Large language models(LLMs)have demonstrated remarkable generalization abilities across multiple tasks in natural language processing(NLP).For multi-step reasoning tasks,chain-of-thought(CoT)prompting facilitates step... Large language models(LLMs)have demonstrated remarkable generalization abilities across multiple tasks in natural language processing(NLP).For multi-step reasoning tasks,chain-of-thought(CoT)prompting facilitates step-by-step thinking,leading to improved performance.However,despite significant advancements in LLMs,current CoT prompting performs suboptimally on smaller-scale models that have fewer parameters.Additionally,the common paradigm of few-shot CoT prompting relies on a set of manual demonstrations,with performance contingent on the quality of these annotations and varying with task-specific requirements.To address these limitations,we propose a select-and-answer prompting method(SAP)to enhance language model performance on reasoning tasks without the need for manual demonstrations.This method comprises two primary steps:guiding the model to conduct preliminary analysis and generate several candidate answers based on the prompting;allowing the model to provide final answers derived from these candidate answers.The proposed prompting strategy is evaluated across two language models of varying sizes and six datasets.On ChatGLM-6B,SAP consistently outperforms few-shot CoT across all datasets.For GPT-3.5,SAP achieves comparable performance to few-shot CoT and outperforms zero-shot CoT in most cases.These experimental results indicate that SAP can significantly improve the accuracy of language models in reasoning tasks. 展开更多
关键词 zero-shot learning large language model(LLM) reasoning problem chain-of-thought(CoT)prompting
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