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Important Date
July 18-21, 2011
22 joint conferences

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ICAI'11 - The 2011 International Conference on Artificial Intelligence

Last modified 2010-12-12 15:18

ICAI'11 is the 13th annual conference




    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

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