The Artificial Intelligence Research Laboratory is part of the Center for Computational Intelligence, Learning, and Discovery at Iowa State University. The laboratory is directed by Dr. Vasant Honavar. The current research foci of the Artificial Intelligence Research Laboratory include:
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Artificial Intelligence: Intelligent agent architectures, Multi-agent organizations, Inter-agent interaction, and Multi-agent coordination, Logical, probabilistic, and decision-theoretic knowledge representation and inference, Neural and computational models of memory, learning, motivation and reward, knowledge representation and inference, perception and action.
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Bioinformatics and Computational Systems Biology: Data-driven discovery of macromolecular sequence-structure-function-interaction-expression relationships, identification of sequence and structural correlates of protein-protein , protein-RNA, and protein-DNA interactions, protein sub-cellular localization, automated protein structure and function annotation, modeling and inference of genetic regulatory networks from gene expression (micro-array, proteomics) data, modeling and inference of signal transduction and metabolic pathways.
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Data Mining: Design, analysis, implementation, and evaluation of algorithms and software for data-driven knowledge acquisition, data and knowledge visualization, and collaborative scientific discovery from semantically heterogeneous, distributed data and knowledge sources, Applications to data-driven knowledge acquisition tasks in bioinformatics, medical informatics, geo-informatics, environmental informatics, chemo-informatics, security informatics, social informatics, critical national infrastructure (communication networks, energy networks) e-government, e-commerce, and e-science.
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Machine Learning: Statistical, information theoretic, linguistic and structural approaches to machine learning, Learning and refinement of bayesian networks, causal networks, decision networks, neural networks, support vector machines, kernel classifiers,, multi-relational models, language models (n-grams, grammars, automata), Learning classifiers from attribute value taxonomies and partially specified data; Learning attribute value taxonomies from data; Learning classifiers from sequential and spatial data; Learning relationships from multi-modal data (e.g., text, images), Learning classifiers from distributed data, multi-relational data, and semantically heterogeneous data; Incremental learning, Ensemble methods, multi-agent learning, selected topics in computational learning theory.
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Semantic Web: Ontology-based user and query-centric approaches to information integration and acquisition of sufficient statistics for learning from data under different access and resource constraints from heterogeneous, distributed, autonomous, ubiquitous information sources, sensor networks, peer-to peer networks; description logics, ontology design, ontology tools, ontology-extended information sources, ontology-extended workflow components, ontology-extended agents and services, semantic workflow composition.
- Selected Topics in: Biological Computation, Evolutionary, Cellular and Neural Computation, Complex Adaptive Systems, Sensory systems and behavior evolution, Language evolution, Mimetic evolution, Computational Semiotics including origins and use of signs, emergence of semantics; Computational organization theory, Computational Neuroscience, Computational models of creativity, Computational models of discovery.
The laboratory's research is funded in part by grants from the National Science Foundation, the National Institutes of Health, the US Department of Agriculture, and Iowa State University.
Additional information about current projects in the Artificial Intelligence Research Laboratory can be found on the projects and publications pages.