General Track Sessions
Papers may be submitted to one of the following General Tracks, or to an Invited Session. Only make one submission and select the track or session which best matches the topic of the paper.
The General Tracks are as follows:-
Generic Tracks
G1: Knowledge-Based and Hybrid Intelligent Systems
This track focuses on AI systems that use knowledge and reasoning, including deductive, inductive and abductive methods, to support intelligent decision-making. This track includes approaches based on rules, logic, ontologies, and other symbolic representations. The track also covers hybrid intelligent systems, where symbolic knowledge is combined with complementary AI methods, such as statistical or learning-based components, to improve reasoning, decision support, or explainability. In these hybrid systems, the knowledge-based component is central, and the additional methods are used to enhance or extend the capabilities of symbolic reasoning.
We encourage submissions that advance knowledge representation, reasoning methods, and symbolic or knowledge-driven hybrid AI that supports transparent and trustworthy decisions.
Focus areas: Knowledge representation, symbolic reasoning, and hybrid AI methods that build on explicit knowledge.
- Symbolic AI, knowledge representation and knowledge graphs
- Reasoning methods and strategies including deductive, inductive, abductive, non-monotonic, case-based and stream reasoning
- Learning-Based Reasoning and Hybrid Reasoning with AI agents, Agentic AI
- Hybrid intelligent systems
- Fuzzy logic and uncertainty reasoning
- Knowledge-based and decision-support systems
- Logic programming and reasoning strategies
- Automated reasoning, theorem proving, planning or scheduling
- Semantic technologies and semantic web reasoning
- Explainability based on knowledge and reasoning
- Intelligent agents, rational agents, goal-based agents and multi-agent systems
G2: Intelligent Information and Generative Systems
This track encompasses both foundational theoretical research and applied studies, with a focus on advanced methodologies and technologies in intelligent information, generative systems, and engineering systems designed to address complex real-world problems. Within this context, information intelligence is defined as the systematic collection, analysis, and interpretation of data to generate knowledge that supports informed decision-making, strategic planning, and value creation for businesses, organizations, and individuals. This track encourages submissions employing AI-driven techniques such as large language models (LLMs), natural language processing, generative AI and AI-based optimization, particularly where these methods enable the transformation of raw data into meaningful information and actionable insights.
We welcome contributions that present innovative methods leveraging advanced intelligent information, generative, and engineering approaches including AI-driven techniques such as LLMs, NLP, generative AI, and AI-based optimization to convert data into meaningful knowledge and actionable insights for tackling complex real-world problems across diverse application domains.
Focus areas:
- Intelligent information systems
- Generative AI systems
- Domain-Specialized LLMs and Knowledge-Augmented Language Models for Intelligent information systems
- LLM-Driven Autonomous Agents and Workflow Automation
- AI-Enhanced Knowledge Management
- Human-AI Collaboration and Interactive Intelligent Systems
- Edge and Distributed Intelligent Information Processing
- Smart business, smart organizations, and digital transformation
- Decision-support systems for smart commerce, digital marketing, e-business, and e-commerce
- Sustainability, the green economy, and environmentally aware information systems
- E-learning and intelligent educational technologies
G3: Engineering Intelligent Learning Systems
This track focuses on the engineering of intelligent learning systems, covering methods and tools for designing, deploying, and managing Machine Learning, Neural networks, and Deep Learning throughout their full lifecycle. It welcomes research contributions and practical applications that advance the way complex AI systems are designed, integrated, optimized, and maintained, especially those using inductive AI, learning pipelines, interpretable models, and adaptive learning mechanisms.
We encourage submissions that explore how machine learning can analyze and interpret data to identify patterns, anomalies, predictions, and trends, as well as interdisciplinary research that applies ML in different domains. Contributions that emphasize the design, scalability, robustness, and trustworthiness of intelligent learning systems are particularly welcome.
Focus areas but not limited to:
- Neural and Deep Learning
- Statistical machine learning
- Knowledge Discovery and Intelligent Data Mining
- Explainable and Interpretable Artificial Intelligence
- Text mining
- Probabilistic and neural hybrid models
- Graph Learning / GNNs
- Federated Learning and distributed IA
- Distributed and parallel learning algorithms
- Multimodal learning
G4: Industry Applications
This track covers Industry Applications, and organizational applications. Contributions describing industry application techniques applied to real-world problems and interdisciplinary research involving industry applications in different application fields e.g., control systems, robot systems and other devices, with special emphasis on industry, are particularly encouraged.
Focus areas:
- Safety and security, cyber security
- Medical and Health Care Systems
- Culture, Arts and Society
- Industrial Control
- Fault Diagnosis
- Environmental Monitoring
- Power Electronics & Drives, High Voltage Systems
- Robotics
- Engine Control and Vehicle Applications, Smart Vehicles and AGVs
- Industrial communication standards (OPCUA, MqTT, DDS)
- Virtual Minifactories
- Industry 4.0 and 5.0 applications
- Financial & Stock Market technologies and applications
- Dashboards, real time networking
- Multisensor Time Series Processing and Prediction
- Multi-Sensor Information Systems
** PLEASE NOTE:
- Do not submit the same paper to more than one General Track or Invited Session as they may be deleted from the conference.
- We may re-allocate papers to more appropriate tracks if we feel it necessary.