Artificial Intelligence: Fundamental Research and Emerging Applications in the Era of Autonomous Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 May 2027 | Viewed by 390
Special Issue Editor
Interests: human-centered systems; machine learning; data science; distributed computing; blockchain
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue invites high-quality original research and comprehensive reviews exploring the rapidly evolving landscape of Artificial Intelligence, emphasizing both foundational breakthroughs and transformative applications. We seek to bridge the gap between theoretical innovations, such as neuromorphic computing, causal inference and physics-informed neural networks and practical implementations that define the next generation of intelligent systems. While the issue welcomes advancements in Large Language Models and Autonomous Agents, it specifically encourages contributions on the interoperability and reliability of AI. This includes emerging standards like the Model Context Protocol, frameworks for open-source agentic ecosystems such as OpenClaw and LangGraph and robust methodologies for mitigating hallucinations through Retrieval-Augmented Generation, Knowledge Graphs, Factuality-Enhanced Decoding and Self-Correction loops. Our goal is to highlight how diverse AI architectures can solve complex real-world challenges while ensuring robustness, scalability and ethical alignment.
Topics of Interest include (but are not limited to):
- Reliable & Fact-Grounded AI: Advanced techniques for hallucination detection, mitigation and verification in generative systems.
- Autonomous Systems & Open Ecosystems: Design of agentic workflows and contributions to open-source frameworks like OpenClaw.
- Interoperable AI Architectures: Standardizing data and tool interfaces through the Model Context Protocol and similar integration layers.
- Neuro-Symbolic & Causal AI: Integrating logic-based reasoning and causal discovery with deep learning for better interpretability.
- Edge Intelligence & Model Compression: Efficient deployment of high-performance models on resource-constrained devices.
- AI for Science: Applications in drug discovery, materials science and climate modeling using physics-aware constraints.
- Human-Centric AI & Safety: Innovations in Reinforcement Learning from Human Feedback, constitutional AI and bias mitigation.
- Scalable Cloud-AI Synergy: Architectures for distributed training and low-latency inference in multi-cloud environments.
- Privacy-Preserving Machine Learning: Federated learning, differential privacy and encrypted computation in sensitive domains.
Prof. Dr. Wenbing Zhao
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- autonomous systems
- model context protocol (MCP)
- neuromorphic computing
- causal inference
- retrieval-augmented generation (RAG)
- knowledge graphs
- federated learning
- AI safety & reliability
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