Artificial Intelligence (AI) systems are computational frameworks designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. These systems integrate algorithms, data, and computing power to enable machines to adapt and improve their performance over time. AI systems encompass a wide range of technologies, including machine learning, natural language processing, computer vision, and autonomous robotics, and they are increasingly applied in domains such as healthcare, finance, cybersecurity, transportation, and smart cities. As they continue to evolve, AI systems are not only transforming industries and enhancing human capabilities but also raising important considerations around ethics, transparency, and trustworthiness.

The International Conference on Artificial Intelligence Systems (AIS 2026) aims to bring together leading researchers, academics, and practitioners from around the world to share their latest findings, innovations, and applications in Artificial Intelligence and intelligent systems. AIS 2026 provides a premier interdisciplinary platform for presenting advances and exchanging insights on the theoretical foundations, cutting-edge technologies, and real-world implementations of AI. AIS 2026 will feature keynote talks, technical paper sessions, workshops, and tutorials covering a wide range of AI topics. We invite submissions of high-quality research papers describing original and unpublished results in all areas of Artificial Intelligence Systems. addresses the use of advanced intelligent systems in providing cybersecurity solutions in many fields, and the challenges, approaches, and future directions. We invite the submission of original papers on all topics related to Intelligent Systems with special interest in but not limited to:

Machine Learning and Data-Driven Systems
Deep Learning, Representation Learning, and Transfer Learning
Reinforcement Learning and Adaptive Systems
Federated, Distributed, and Edge AI
Reinforcement Learning and Autonomous Agents
Federated Learning and Distributed AI Systems
AI Applications and Intelligent Systems
Scalable and High-Performance AI Architectures
Edge AI, Cloud AI, and Embedded Intelligence
Optimization and Resource-Aware AI Systems

Natural Language and Multimodal AI
Natural Language Processing and Understanding
Speech Recognition and Conversational Agents
Multimodal Learning and Vision-Language Systems
Knowledge Representation and Reasoning
Data Mining, Knowledge Discovery, and Information Retrieval
Automated Planning and Scheduling

Generative AI, Foundation Models, and Large Language Models
Generative AI and Large Language Models (LLMs)
Explainable and Responsible AI
Federated, Distributed, and Edge AI
Reinforcement Learning and Autonomous Agents
AI for Decision Support Systems
Natural Language Processing and Computational Linguistics
Multilingual and Low-Resource Language Models
Human-Computer Interaction (HCI) and Assistive Technologies
Conversational AI and Dialogue Systems
Computational Language and Human-Centered Systems
Prompt Engineering and Fine-tuning
Multimodal LLMs
Agentic AI

Computer Vision, Image and Video Processing
Image and Video Understanding
Object Detection, Recognition, and Tracking
Multimodal Learning and Vision-Language Systems
3D Vision and Scene Understanding
Affective Computing and Emotion Recognition

Artificial Intelligence Applications
Robotics and Autonomous Systems
AI for Healthcare, Smart Cities, and Transportation
AI in Finance, Education, and Industry 4.0
AI for Cybersecurity, Privacy, and Digital Trust
AI Infrastructure and Engineering
Recommender Systems and Personalization
AI for Social Good and Humanitarian Applications
Emerging Trends and Future Directions in AI Systems

Artificial Intelligence Security
Responsible and Trustworthy AI
Explainable AI and Transparency
Ethical and Societal Implications of AI
AI Safety, Robustness, and Security
AI Policy, Regulation, and Governance