ICAI 2017 - 19th Int'l Conf on Artificial Intelligence

Artificial Intelligence (AI) is the science and engineering of making intelligent machines and systems. This is an important multi-disciplinary field which is now an essential part of technology industry, providing the heavy lifting for many of the most challenging problems in computer science. Since Machine Learning has strong ties with AI, the conference also covers the field of Machine Learning. The list of topics below is by no means meant to be exhaustive.
Artificial Intelligence:
- Brain models, Brain mapping, Cognitive science
- Natural language processing
- Fuzzy logic and soft computing
- Software tools for AI
- Expert systems
- Decision support systems
- Automated problem solving
- Knowledge discovery
- Knowledge representation
- Knowledge acquisition
- Knowledge-intensive problem solving techniques
- Knowledge networks and management
- Intelligent information systems
- Intelligent data mining and farming
- Intelligent web-based business
- Intelligent agents
- Intelligent networks
- Intelligent databases
- Intelligent user interface
- AI and evolutionary algorithms
- Intelligent tutoring systems
- Reasoning strategies
- Distributed AI algorithms and techniques
- Distributed AI systems and architectures
- Neural networks and applications
- Heuristic searching methods
- Languages and programming techniques for AI
- Constraint-based reasoning and constraint programming
- Intelligent information fusion
- Learning and adaptive sensor fusion
- Search and meta-heuristics
- Multisensor data fusion using neural and fuzzy techniques
- Integration of AI with other technologies
- Evaluation of AI tools
- Social intelligence (markets and computational societies)
- Social impact of AI
- Emerging technologies
- Applications (including: computer vision, signal processing, military, surveillance, robotics, medicine, pattern recognition, face recognition, finger print recognition, finance and marketing, stock market, education, emerging applications, ...)
Machine Learning; Models, Technologies and Applications:
- Statistical learning theory
- Unsupervised and Supervised Learning
- Multivariate analysis
- Hierarchical learning models
- Relational learning models
- Bayesian methods
- Meta learning
- Stochastic optimization
- Simulated annealing
- Heuristic optimization techniques
- Neural networks
- Reinforcement learning
- Multi-criteria reinforcement learning
- General Learning models
- Multiple hypothesis testing
- Decision making
- Markov chain Monte Carlo (MCMC) methods
- Non-parametric methods
- Graphical models
- Gaussian graphical models
- Bayesian networks
- Particle filter
- Cross-Entropy method
- Ant colony optimization
- Time series prediction
- Fuzzy logic and learning
- Inductive learning and applications
- Grammatical inference
- Graph kernel and graph distance methods
- Graph-based semi-supervised learning
- Graph clustering
- Graph learning based on graph transformations
- Graph learning based on graph grammars
- Graph learning based on graph matching
- Information-theoretical approaches to graphs
- Motif search
- Network inference
- Aspects of knowledge structures
- Computational Intelligence
- Knowledge acquisition and discovery techniques
- Induction of document grammars
- General Structure-based approaches in information retrieval, web authoring, information extraction, and web content mining
- Latent semantic analysis
- Aspects of natural language processing
- Intelligent linguistic
- Aspects of text technology
- Biostatistics
- High-throughput data analysis
- Computational Neuroscience
- Computational Statistics