BioInspired! So What? Now What!

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.

IMPORTANT DATES:
March 10, 2011:    Submission of papers (about 5 to 7 pages)
April 03, 2011:       Notification of acceptance (+/- two days)
April 24, 2011:       Final papers + Copyright/Consent + Registration
July 18-21, 2011:  The 2011 International Conference on Artificial Intelligence (ICAI'11)

SCOPE: Topics of interest include, but are not limited to, the following:
O  Brain models / cognitive science
O  Natural language processing
O  Fuzzy logic and soft computing
O  Software tools for AI
O  Expert systems
O  Decision support systems
O  Automated problem solving
O  Knowledge discovery
O  Knowledge representation
O  Knowledge acquisition
O  Knowledge-intensive problem solving techniques
O  Knowledge networks and management
O  Intelligent information systems
O  Intelligent data mining and farming
O  Intelligent web-based business
O  Intelligent agents
O  Intelligent networks
O  Intelligent databases
O  Intelligent user interface
O  AI and evolutionary algorithms
O  Intelligent tutoring systems
O  Reasoning strategies
O  Distributed AI algorithms and techniques
O  Distributed AI systems and architectures
O  Neural networks and applications
O  Heuristic searching methods
O  Languages and programming techniques for AI
O  Constraint-based reasoning and constraint programming
O  Intelligent information fusion
O  Learning and adaptive sensor fusion
O  Search and meta-heuristics
O  Swarm Optimization
O  Multisensor data fusion using neural and fuzzy techniques
O  Integration of AI with other technologies
O  Evaluation of AI tools
O  Social intelligence (markets and computational societies)
O  Social impact of AI
O  Emerging technologies
O  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, ...)
O  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
     . 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

Thanks to A. M. G. Solo and the Biomimetics mail list for the pointer!