ICAI'11 - The 2011 International Conference on Artificial Intelligence
Last modified
2010-12-12 15:18
ICAI'11 is the 13th annual conference
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You are invited to submit a full paper for consideration. All
accepted papers will be published in the ICAI conference
proceedings (in printed book form; later, the proceedings will
also be accessible online). Those interested in proposing
workshops/sessions, should refer to the relevant sections that
appear below.
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
- Topics on satisfiability
- 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
- Topics on satisfiability
- 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
Click Here for more details
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