Advances in Fuzzy Intelligence and Non-Classical Logical Computing

Dear Colleagues,

This Special Issue, hosted by the journal Mathematics and jointly supported by the Operations Research Society of China (Fuzzy Information and Engineering Branch), the Artificial Intelligence Foundation Committee of the Chinese Association for Artificial Intelligence, and the Non-Classical Logic and Computation Committee of the Chinese Association of Logic, aims to promote the latest theoretical and methodological advances at the intersection of fuzzy systems, non-classical logics, and artificial intelligence foundations.

We welcome original research articles, reviews, and application reports that advance the theoretical foundation, algorithm design, and interdisciplinary application of fuzzy intelligence and logical reasoning in uncertain and complex systems.

Topics of interest include (but are not limited to) the following:

A. Fuzzy Information and Engineering

A1. Theoretical Models of Fuzzy Information;

A1.1: Fuzzy Sets, Measures, and Inference Theory;

A1.2: Fuzzy Granulation, Similarity Measures, and Relational Models;

A1.3: Linguistic Variables and Semantic Interpretation;

A1.4: Uncertainty Modeling and Information Fusion in Fuzzy Systems;

A1.5: Statistical and Data-Driven Modeling for Fuzzy Information;

A2. Fuzzy Decision Making and Optimization;

A2.1: Multi-Attribute Decision-Making under Fuzziness;

A2.2: Fuzzy Multi-Objective Optimization and Evolutionary Algorithms;

A2.3: Fuzzy AHP, Fuzzy TOPSIS, and Aggregation Models;

A2.4: Group Decision-Making with Linguistic Preferences;

A2.5: Fuzzy Constrained Optimization and Game-Theoretic Models;

A3. Engineering Applications of Fuzzy Information;

A3.1: Fuzzy Technologies in Smart Manufacturing and Industry 4.0;

A3.2: Fuzzy Control Systems in Automation and Robotics;

A3.3: Fuzzy Data Mining in Healthcare, Finance, and Transportation;

A3.4: Fuzzy Models in Energy Management and Environmental Engineering;

A3.5: Integrated Fuzzy Modeling for Complex Engineering Systems.

B. Foundations of Artificial Intelligence

B1. Logical Foundations and Computability;

B1.1: Axiomatic Models of Artificial Intelligence;

B1.2: Formal Systems and Proof Theory in AI;

B1.3: Turing Computability, Decidability, and Complexity Theory;

B2. Knowledge Representation and Ontologies;

B2.1: Description Logics and Semantic Web Technologies;

B2.2: Ontology Construction and Concept Hierarchies;

B2.3: Symbolic vs. Sub-symbolic Representation Frameworks;

B3. Reasoning Mechanisms and Formal Inference;

B3.1: Deductive and Inductive Reasoning;

B3.2: Logic Programming and Answer Set Programming;

B3.3: Inference Engines and Explanation-Based Reasoning;

B4. Learning Theory and Generalization;

B4.1: Computational Learning Theory (PAC, VC-dimension, etc.);

B4.2: Inductive Logic Learning and Statistical Relational Learning;

B4.3: Theoretical Aspects of Deep and Symbolic Learning;

B5. Intelligent Agents and Cognitive Modeling;

B5.1: Agent Logics: Belief, Desire, Intention (BDI) Models;

B5.2: Cognitive Architectures (e.g., SOAR, ACT-R);

B5.3: Formal Modeling of Perception, Memory, and Decision.

C. Non-Classical Logic and Computational Methods

C1. Non-Classical Logical Systems;

C1.1: Modal Logic and Temporal Logic in Dynamic Systems;

C1.2: Intuitionistic, Many-Valued, and Paraconsistent Logics;

C1.3: Hybrid Logics and Fuzzy-Modal Extensions;

C2. Non-Monotonic and Argumentative Reasoning;

C2.1: Default Logic and Autoepistemic Reasoning;

C2.2: Belief Revision and Defeasible Reasoning;

C2.3: Structured Argumentation and Logic-Based Debating Systems;

C3. Causal and Counterfactual Models;

C3.1: Formal Models of Causality (e.g., Pearl’s Framework);

C3.2: Counterfactual Reasoning in AI Planning and Diagnosis;

C3.3: Causal Logic Programming and Explanatory Reasoning;

C4. Logic Programming and Computational Semantics;

C4.1: Non-Classical Constraint Satisfaction Problems;

C4.2: Semantic Models for Inconsistent or Incomplete Data;

C4.3: Applications in Robotics, Planning, and XAI Systems.

Prof. Yu-Bin Zhong
Dr. Yi Xiang
Guest Editors

Manuscript Submission Information

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• fuzzy logic and reasoning
• causal models
• computability
• non-classical logics
• artificial intelligence foundations
• factor neural networks
• knowledge representation
• uncertainty modeling