ICAI 2013 - The 2013 International Conference on Artificial Intelligence (ICAI'13)

You are invited to submit a full paper for consideration. All accepted papers will be published in printed conference books/proceedings (they will also be available on the web). The proceedings will be processed for indexing into science citation databases that track citation frequency/data for each paper. These science citation databases include: Inspec / IET / The Institute for Engineering and Technology, CiteSeerX citation index, Google Scholar, Microsoft Academic Search, and other science databases. Like prior years, extended versions of selected papers (about 40%) will appear in journals and edited research books (publishers include: Springer, Elsevier, ...).
Topics of interest include, but are not limited to, the following:
Brain models / 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, ...)
Workshop on Machine Learning; Models, Technologies and Applications:
- General Machine Learning Theory
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
- General Graph-based Machine Learning Techniques
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
General theoretical aspects of graph learning
Statistical modeling of graphs
Information-theoretical approaches to graphs
Motif search
Network inference
General issues in graph and tree mining
- Machine Learning Applications
Aspects of knowledge structures
Computational Finance
Computational Intelligence
Knowledge acquisition and discovery techniques
Induction of document grammars
Supervised and unsupervised classification of web data
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
Computational vision
Bioinformatics and computational biology
Biostatistics
High-throughput data analysis
Biological network analysis: protein-protein networks, signaling networks, metabolic networks, transcriptional regulatory networks
Graph-based models in biostatistics
Computational Neuroscience
Computational Chemistry
Computational Statistics
Systems Biology
Algebraic Biology