Belavkin, R. V. (in press). Maximal Connectivity and Constraints in the Human Brain. InFields Institute Communications: Workshop on Optimization and Data Analysis in Biomedical Informatics(Vol. 63), American Mathematical Society.
Belavkin, R. V. (2011). On Evolution of an Information Dynamic System and its Generating Operator.Optimization Letters. Springer. [DOI: 10.1007/s11590-011-0325-z]
Belavkin, R. V. & Huyck, C. R. (2010). Conflict Resolution and Learning Probability Matching in a Neural Cell-Assembly Architecture.Cognitive Systems Research. Elsevier. [DOI: 10.1016/j.cogsys.2010.08.003].
Belavkin, R. V. (2010). Utility and value of information in cognitive science, biology and quantum theory. In L. Accardi, W. Freudenberg & M. Ohya (Eds.)Quantum Bio-Informatics III(Vol. 26). World Scientific.
Belavkin, R. V. (2010). Information trajectory of optimal learning. In M. J. Hirsch P. M. Pardalos & R. Murphey (Eds.)Dynamics of Information Systems: Theory and Applications(Vol. 40). Springer.
Belavkin, R. V. & Huyck, C. R. (2009). A Model of Probability Matching in a Two-Choice Task Based on Stochastic Control of Learning in Neural Cell-Assemblies. InProceedings of the 9th International Conference on Cognitive Modelling. (PDF, 88Kb)
Belavkin, R. V. (2009). Bounds of optimal learning. InIEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning(pp. 199--204). Nashville, TN, USA, IEEE (2009). (PDF, 167K)
Belavkin, R. V. (2008). The Duality of Utility and Information in Optimally Learning Systems. In 7th IEEE International Conference on 'Cybernetic Intelligent Systems'. Middlesex University, London, UK.
Belavkin, R. V. (2008). Information Evolution of Optimal Learning. In PASCAL 2008 Workshop on 'Approximate Inference in Stochastic Processes and Dynamical Systems'. Cumberland Lodge, UK.
Belavkin, R. V. (2007). Do Neural Models Scale up to a Human Brain? In Proceedings of The International Joint Conference on Neural Networks. IEEE.
Belavkin, R. V. (2006). Towards a Theory of Decision-Making without Paradoxes. In D. Fum, F.D. Missier& A. Stocco (Eds.) Proceedings of the Seventh International Conference on Cognitive Modelling (pp. 38-43). Trieste, Italy: Edizioni Goliardiche (ISBN 88-7873-031-9).
Belavkin, R. V. (2005). Acting irrationally to improve performance in stochastic worlds. In M. Bramer, F. Coenen, & T. Allen (Eds.) Proceedings of AI-2005, the 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. (Vol. XXII, pp. 305--316). Cambridge: Springer (ISBN 1-84628-225-X) www.cs.mdx.ac.uk/staffpages/rvb/publications/rvb-sgai05.pdf.
Belavkin, R. V. (2005). A test for irrational thinking. AISB Quarterly, 122. ISSN 0268-4179.
Belavkin, R. V. (2005). Entropy and Information in Models of Learning Behaviour. AISB Quarterly, 119:5. ISBN 0268-4179.
Belavkin, R. V. & Ritter, F. E. (2004). OPTIMIST: A New Conflict Resolution Algorithm for ACT--R. In Proceedings of the Sixth International Conference on Cognitive Modelling. USA, (PDF, 477Kb).
Belavkin, R. V. (2004). On Relation between Emotion and Entropy. In C.Johnson (Ed.), Proceedings of the AISB'04 Symposium on Emotion, Cognition and Affective Computing (pp. 1--8). Leeds, UK. ISBN 1-902956-36-6 (PDF, 104Kb).
Belavkin, R. V. (2003). On Emotion, Learning and Uncertainty: A Cognitive Modelling Approach. PhD Thesis (PDF, 2.17Mb).
Belavkin, R. V. (2003). Conflict Resolution by Random Estimated Costs. In D. Al-Dabass (Ed.), Proceedings of 17th European Simulation Multiconference (pp. 105--110). Nottingham, England. ISBN 3-936150-25-7 (PDF 465K).
Belavkin, R. V.& Ritter, F. E. (2003). The Use of Entropy for Analysis and Control of Cognitive Models. In F. Detje, D. D\"orner,& H. Schaub (Eds.), Proceedings of the Fifth International Conference on Cognitive Modelling (pp. 21--26). Bamberg, Germany: Universit\"ats-Verlag Bamberg (PDF, 197K).
Belavkin, R. V. (2001). Modelling the Inverted-U Effect in ACT-R. In E. M. Altmann, A. Cleeremans, C. D. Schunn, & W. D. Gray (Eds.), Proceedings of the 2001 Fourth International Conference on Cognitive Modelling (pp. 275--276). Mahwah, New Jersey, London: Lawrence Erlbaum (PDF, 74K).
Belavkin, R. V. (2001). The role of emotion in problem solving. In Proceedings of the AISB'01 symposium on Emotion, Cognition and Affective Computing (pp. 49--57). Heslington, York, England (PDF, 715K).
