Alan Blair's Publications

Neural Networks, Deep Learning and Language Processing

[HB16] A. Hadjiivanov & A. Blair, 2016. Complexity-based speciation and genotype representation for neuroevolution, Congress on Evolutionary Computation (CEC 2016), 3092-3101.

[KB14] A. Knittel & A. Blair, 2014. Coarse and Fine Learning in Deep Networks, International Joint Conference on Neural Networks (IJCNN 2014), 792-799.

[KB12] A. Knittel & A. Blair, 2012. An Abstract Deep Network for Image Classification, 25th Australasian Joint Conference on Artificial Intelligence, 156-169.

[BL09] A. Blair & G. Li, 2009. Training of Recurrent Internal Symmetry Networks by Backpropagation, International Joint Conference on Neural Networks (IJCNN 2009), 353--358.

[PB06] C. Phua & A. Blair, 2006. An improved minibrain that learns through both positive and negative feedback, International Joint Conference on Neural Networks (IJCNN 2006), 812-819.

[CB03] S. Chalup & A. Blair, 2003. Incremental training of first order recurrent neural networks to predict a context-sensitive language, Neural Networks 16, 955-972.

[BI03] A.D. Blair & J. Ingram, 2003. Learning to predict the phonological structure of English loanwords in Japanese, Applied Intelligence 19, 101-108.

[BB03] M. Boden & A.D. Blair, 2003. Learning the dynamics of embedded clauses, Applied Intelligence 19, 51-63.

[WBB01] J. Wiles, A.D. Blair & M. Bodén, 2001. Representation Beyond Finite States: Alternatives to Push-Down Automata, in J.F. Kolen & S.C. Kremer (Eds.) A Field Guide to Dynamical Recurrent Networks, IEEE Press, 129-142.

[WCHBN00] J. Wiles, H. Chenery, J. Hallinan, A. Blair, A. & D. Naumann, 2000. Effects of damage to the CDM Stroop model, Proc. 5th Conference of the Australasian Cognitive Science Society.

[TBW00] B. Tonkes, A.D. Blair & J. Wiles, 2000. Evolving learnable languages, Advances in Neural Information Processing Systems 12 (NIPS 12), MIT Press, 66-72.

[CB99] S. Chalup & A.D. Blair, 1999. Hill climbing in recurrent neural networks for learning the anbncn language, Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP'99), 508-513.

[BWTB99] M. Bodén, J. Wiles, B. Tonkes & A.D. Blair, 1999. Learning to predict a context-free language: Analysis of dynamics in recurrent hidden units, (ICANN'99), Edinburgh, 359-364.

[TBW99] B. Tonkes, A.D. Blair & J. Wiles, 1999. A paradox of neural encoders and decoders or Why don't we talk backwards? Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'98) LNCS 1585, 357-364.

[IB99] N. Ireland & A.D. Blair, 1999. Target signal selection for a neural network based financial classifier, ICSC Symposium on Soft Computing in Financial Markets.

[BI98] A.D. Blair & J. Ingram, 1998. Loanword formation: a neural network approach, Proceedings of the Fourth Meeting of the ACL Special Interest Group in Computational Phonology, Montreal, 1998, 45-54.

[TBW98] B. Tonkes, A.D. Blair & J. Wiles, 1998. Inductive bias in context-free language learning, Ninth Australian Conference on Neural Networks, Brisbane, Australia.

[BP97a] A. Blair & J. Pollack, 1997. Analysis of dynamical recognizers, Neural Computation 9(5), 1997, 1127-1142.

[BP97b] A.D. Blair & J.B. Pollack, 1997. Quasi-orthogonal maps for dynamic language recognition, Fourth International Conference on Neural Information Processing (ICONIP'97), 1065-1067.

[B95a] A.D. Blair, 1995. Two layer digital RAAM, 17th Annual Conference of the Cognitive Science Society, Pittsburgh, 478-481.

[B95b] A.D. Blair, 1995. Adelic path space integrals, Reviews in Mathematical Physics 7(1), 1995, 21-49.

Evolution and Modularity

[VSB17] D. Vickers, J. Soderlund & A. Blair, 2017. Co-Evolving Line Drawings with Hierarchical Evolution, Australasion Conference on Artificial Life and Computational Intelligence, LNAI 10142, 39-49.

[SVB16] J. Soderlund, D. Vickers & A. Blair, 2016. Parallel Hierarchical Evolution of String Library Functions, Parallel Problem Solving from Nature LNCS 9921, 281-291.

[B15] A. Blair, 2015. Trasgenic Evolution for Classification Tasks with HERCL, Australasian Conference on Artificial Life and Computational Intelligence, LNAI 8955, 185-195.

[B14] A. Blair, 2014. Incremental Evolution of HERCL Programs for Robust Control, Genetic and Evolutionary Computation Conference Companion, 27-28.

[OBC14] O. Coleman, A. Blair & J. Clune, 2014. Automated Generation of Environments to Test the General Learning Capabilities of AI Agents, Genetic and Evolutionary Computation Conference (GECCO 2014), 161-168.

[B13] A. Blair, 2013. Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination, IEEE Congress on Evolutionary Computation (CEC 2013), 605-612.

[CB12] O. Coleman & A. Blair, 2012. Evolving Plastic Neural Networks for Online Learning: Review and Future Directions, 25th Australasian Joint Conference on Artificial Intelligence, 326-337.

[HB06a] R. Harper & A. Blair, 2006. Dynamically Defined Functions in Grammatical Evolution, IEEE Congress on Evolutionary Computation (CEC 2006), 2638-2645.

[HB06b] R. Harper & A. Blair, 2006. A self-selecting crossover operator, IEEE Congress on Evolutionary Computation (CEC 2006), 1420--1427.

[HB05] R. Harper & A. Blair, 2005. A structure preserving crossover in Grammatical Evolution, IEEE Congress on Evolutionary Computation (CEC 2005), 2537--2544.

Games and Coevolution

[RB16] D. Real & A. Blair, 2016. Learning a multi-player Chess game with TreeStrap, IEEE Congress on Evolutionary Computation (CEC 2016), 617-623.

[GRA11] D. Gurto, M. Ryan & A. Blair, 2011. Crafty: Dynamic vendor pricing in computer role-playing games, 6th International Conference on Foundations of Digital Games, 286-288.

[VSUB09] J. Veness, D. Silver, W. Uther & A. Blair, 2009. Bootstrapping from game tree search, Advances in Neural Information Processing Systems (NIPS 22), 1937-1945.

[B08] A. Blair, 2008. Learning position evaluation for Go with internal symmetry networks, IEEE Symposium on Computational Intelligence and Games (CIG 2008), 199-204. (see also [B09], unpublished).

[VB07] J. Veness & A. Blair, 2007. Effective use of transposition tables in stochastic game tree search, IEEE Symposium on Computational Intelligence and Games (CIG 2007), 112-116.

[OB02] T. Ord & A. Blair, 2002. Exploitation and peacekeeping: introducing more sophisticated interactions to the Iterated Prisoner's Dilemma, IEEE Congress on Evolutionary Computation (CEC 2002), 1606-1611.

[SBB01] D. Shaw, N. Barnes & A. Blair, 2001. Creating Characters for Dynamic Stories in Interactive Games, International Conference on Application Development of Computer Games in the 21st Century.

[SBP01] E. Sklar, A. Blair & J. Pollack, 2001. Training intelligent agents using human data collected on the Internet, in J.Liu, N. Zhong, Y. Tang & P. Wang (Eds.) Agent Engineering, World Scientific, 201-226.

[SBFP99] E. Sklar, A.D. Blair, P. Funes & J.B. Pollack, 1999. Training intelligent agents using human Internet data, Proceedings of the First Asia-Pacific Conference on Intelligent Agent Technology, World Scientific, 354-363.

[BS99] A.D. Blair & E. Sklar, 1999. Exploring evolutionary learning in a simulated hockey environment, Congress on Evolutionary Computation, 197-203.

[BSF99] A.D. Blair, E. Sklar & P. Funes, 1999. Co-evolution, determinism and robustness, Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'98) LNCS 1585, 389-396.

[B99] A.D. Blair, 1999. Co-evolutionary learning - lessons for human education? Fourth Conference of the Australasian Cognitive Science Society, Newcastle, Australia.

[BS98] A.D. Blair & E. Sklar, 1998. The evolution of subtle manoeuvres in simulated hockey, Fifth Conference on Simulation of Adaptive Behavior (SAB'98), Zurich, 280-285.

[SBP98] E. Sklar, A.D. Blair & J.B. Pollack, 1998. Co-evolutionary learning: machines and humans schooling together, Workshop on Current Trends and Applications of Artificial Intelligence in Education, ITESM, Mexico, 98-105.

[PB98] J.B. Pollack & A.D. Blair, 1998. Co-evolution in the successful learning of Backgammon strategy, Machine Learning 32, 225-240.

[BP97c] A.D. Blair & J.B. Pollack, 1997. What makes a good co-evolutionary learning environment? Australian Journal of Intelligent Information Processing Systems 4, 166-175.

[PB97] J.B. Pollack & A.D. Blair, 1997. Why did TD-Gammon work? Advances in Neural Information Processing Systems (NIPS 9), 10-16.

[PBL97] J.B. Pollack, A.D. Blair & M. Land, 1997. Coevolution of a Backgammon player, Fifth International Conference on Artificial Life, MIT Press, 92-98.

Robot Navigation

[TB07a] B. Tonkes & A. Blair, 2007. Decentralised data fusion with exponentials of polynomials, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), 3727--3732.

[TB07b] B. Tonkes & A. Blair, 2007. Deriving Sensor Models and Non-Linear Filtering for Exponentials of Polynomials, Australasian Conference on Robotics and Automation (ACRA 2007).

[KWB06] K.-M. Kiang, R. Willgoss & A. Blair, 2006. Distinctness analysis on natural landmark descriptors, International Conference on Field and Service Robotics (FSR 2006), 67-78.

[KWB05] K.-M. Kiang, R. Willgoss & A. Blair, 2005. Texture and distinctness analysis for natural feature extraction, Australasian Conference on Robotics and Automation (ACRA 2005).

[KWB04] K.-M. Kiang, R. Willgoss & A. Blair, 2004. Distinctive feature analysis of natural landmarks as a front end for SLAM applications, 2nd International Conference on Autonomous Robots and Agents (ICARA 2004), 206-211.

[TBB03] J. Thomas, A. Blair & N. Barnes, 2003. Towards an efficient optimal trajectory planner for multiple mobile robots, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), 2291-2296.

[VB01] S. Versteeg & A. Blair, 2001. Getting the job done in a hostile environment, 14th Australian Joint Conference on Artificial Intelligence (LNAI 2256).

[HBWK00] A. Howard, A. Blair, D. Walter & E. Kazmierczak, 2000. Motion control for fast mobile robots: a trajectory-based approach, Australian Conference on Robotics and Automation (ACRA 2000).


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