ICGI 2010 Invited Lectures

Abstract Molecular biology is full of linguistic metaphors, from the language of DNA to the genome as "book of life." Certainly the organization of genes and other functional modules along the DNA sequence invites a syntactic view, which can be seen in certain tools used in bioinformatics such as hidden Markov models. It has also been shown that folding of RNA structures is neatly expressed by grammars that require expressive power beyond context-free, an approach that has even been extended to the much more complex structures of proteins. Processive enzymes and other "molecular machines" can also be cast in terms of automata. This talk will provide a review of linguistic approaches to molecular biology, and perspectives on potential future applications of grammars and automata in this field.

Brief Bio David Searls received undergraduate degrees in Philosophy and Life Sciences from the Massachusetts Institute of Technology, and a PhD in Biology from the Johns Hopkins University. Following a postdoctoral fellowship at the Wistar Institute in Philadelphia he completed a Master's in Computer and Information Science at the University of Pennsylvania. He spent seven years doing Artificial Intelligence R&D at Unisys, then co-founded the Computational Biology and Informatics Laboratory at the University of Pennsylvania, with joint faculty appointments in Genetics and Computer and Information Science (where he retains an adjunct appointment). He subsequently spent 13 years at SmithKline Beecham and then GlaxoSmithKline Pharmaceuticals, where he was Senior Vice-President of Bioinformatics, directing scientists and engineers at six sites worldwide. He left GSK in 2008 and is now an independent consultant. He was a founding Director of the International Society for Computational Biology, and serves on advisory boards for the Protein Databank (PDB), the EMBL-EBI UniProt resource, the International Union of Pharmacology (IUPHAR), and the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation. His research interests include the linguistics of biological macromolecules and scientific data integration.

Abstract Games provide ideal environments in which to study machine learning, offering a range of challenging and engaging problems. This talk highlights some examples of recent work in the field of artificial intelligence and games, and in particular relates them to grammatical inference models and algorithms. I will argue that the fields have some synergy which has yet be realised to its full potential, and demonstrate this with some examples including player log analysis and finite state machine induction. The benefit for games is that powerful GI algorithms could be used to learn controllers for non-player characters by example. The benefit for GI is that researchers get to work on a set of hard problems that have industrial relevance.

Brief Bio Simon M. Lucas (SMIEEE) is a professor of computer science at the University of Essex (UK) where he leads the Game Intelligence Group. His main research interests are games, evolutionary computation, and machine learning, and he has published widely in these fields with over 140 peer-reviewed papers, mostly in leading international conferences and journals. He was chair of IAPR Technical Committee 5 on Benchmarking and Software (2002 - 2006) and is the inventor of the scanning n-tuple classifier, a fast and accurate OCR method. He has organised and co-chaired many international conferences. He is an associated editor of IEEE Transactions on Evolutionary Computation, and the Journal of Memetic Computing. He has given invited keynote talks and tutorials at many conferences including IEEE CEC, IEEE CIG, and PPSN. Professor Lucas was recently appointed as the founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games. Simon Lucas was the first chairman of the International Colloquium on Grammatical Inference (ICGI) in 1993 edition at Essex.