How many features does it take to change a lightbulb?

In the 1970s and 80s Cognitive Science and Cognitive Linguistics and Computational Psycholinguistics emerged as the boxes around our disciplines started to become straight-jackets, and research out of one discipline would start to make waves in others. The toy systems of Artificial Intelligence were reaching limits, and introspection by programmers and engineers was reinventing square wheels without any biological plausibility and in ignorance of relevant work across the cognitive sciences, while conversely, work in other fields often lacked the understanding of computability and complexity necessary to ensure that models were realistic and computationally plausible. This is the starting point for the research program I have been undertaking for the last 35 years, seeking to build intelligent computer systems and computational cognitive models. The idea has been to try to build an intelligent system modelled on the way a baby learns about the world, culture, society and language. Conversely, the idea has been to explore theories from psychology, linguistics and neuroscience through the medium of computational models. The primary focus and agenda of our research program are summed up in Powers and Turk (1989): language and ontology are learned together through multimodal association.

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[13]  David M. W. Powers,et al.  Using Grammatical Relations to Automate Thesaurus Construction , 2010, J. Res. Pract. Inf. Technol..

[14]  David M. W. Powers,et al.  Evolutionary feature selection and electrode reduction for EEG classification , 2012, 2012 IEEE Congress on Evolutionary Computation.

[15]  Kerstin Eder,et al.  Improving XCS performance on overlapping binary problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

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[17]  David M. W. Powers,et al.  Spatial and temporal visual speech feature for Chinese phonemes , 2012 .

[18]  David M. W. Powers,et al.  The Problem with Kappa , 2012, EACL.

[19]  Trent W. Lewis,et al.  Designing and Evaluating Interactive Agents as Social Skills Tutors for Children with Autism Spectrum Disorder , 2011 .

[20]  David M. W. Powers,et al.  Unsupervised learning of linguistic structure An empirical evaluation , 2003 .

[21]  Trent W. Lewis,et al.  Comparison of Region Based and Weighted Principal Component Analysis and Locally Salient ICA in Terms of Facial Expression Recognition , 2011, SNPD.

[22]  David M. W. Powers,et al.  Characterization and evaluation of similarity measures for pairs of clusterings , 2009, Knowledge and Information Systems.

[23]  S P Fitzgibbon,et al.  Removal of EEG Noise and Artifact Using Blind Source Separation , 2007, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[24]  David M. W. Powers.  Learning and Application of Differential Grammars , 1997, CoNLL.

[25]  Trent W. Lewis,et al.  Development of a virtual agent based social tutor for children with autism spectrum disorders , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[26]  Trent W. Lewis,et al.  Hybrid world object tracking for a virtual teaching agent , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).