• Craig Thompson and Michael Horsch. “Predicting Good Propagation Methods for Constraint Satisfaction.” In Proceedings of the 25th Canadian Conference on Artificial Intelligence, May 2012, Toronto, Canada (12 pages).
  • Michael Janzen, Michael Horsch, Eric Neufeld. “A Method of Virtual Camera Selection using Soft Constraints.” In Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, 2011 (6 pages).
  • Michael Janzen, Eric Neufeld, and Michael Horsch. “Camera Selection Using SCSPs.” In Proceedings of the 13th International Conference on Computer Graphics and Artificial Intelligence, 2010. (6 pages)
  • E. Neufeld and M. Horsch. 2009. Bayesian Belief Networks. Wiley Encyclopedia of Computer Science and Engineering. 289–298.
  • Kevin Grant and M. C. Horsch. “Efficient Caching in Elimination Trees” Proceedings of the 20th Florida Artificial Intelligence Research Society (FLAIRS) Conference, accepted January, 2007. (6 pages)
  • Kevin Grant and M. C. Horsch. ``Exploiting Dynamic Independence in a Static Conditioning Graph.'' In Proceedings of the Nineteenth Canadian Conference on Artificial Intelligence, June 2006, Laval, Canada, 12 pages. PDF
  • Kevin Grant and M. C. Horsch. "Methods for Constructing Balanced Elimination Trees and Other Recursive Decompositions." In Proceedings of the Nineteenth Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 2006, Melbourne Beach, USA, 6 pages. PDF
  • Jeff R. Long, Michael C. Horsch "A Formalization of Game Balance Principles." In Workshop on Game Theory, Machine Learning and Reasoning under Uncertainty, 19th Annual Conference on Neural Information Processing Systems, December, 2004, Whistler, Canada, 8 pages.
  • Kevin Grant, Michael C. Horsch, "Practical Structures for Inference in Bayesian Networks." In 18th Australian Joint Conference on Artificial Intelligence December 2005, Sydney. PDF
  • Michael C. Horsch, Jingfang Zheng "A Decision Theoretic Meta-Reasoner for Constraint Optimization. In Proceedings of the Eighteenth Canadian Conference on Artificial Intelligence, Victoria, British Columbia, 2004, pp53-65. PDF
  • Jeff R. Long, Michael C. Horsch "A Bayesian model to smooth telepointer jitter." In Proceedings of the Eighteenth Canadian Conference on Artificial Intelligence, Victoria, British Columbia, 2004, pp 108-119. PDF
  • David Mould, Michael C. Horsch, "An Hierarchical Terrain Representation for Approximately Shortest Paths" In Proceedings of the Eighth Pacific Rim International Conference on Artificial Intelligence, August 2004, Auckland, pp. 104-113 PDF
  • Jingfang Zheng, Michael C. Horsch, "A Comparison of Consistency Propagation Algorithms in Constraint Optimization." In Proceedings of the Sixteenth Canadian Conference on Artificial Intelligence, June 2003, Halifax, pp. 160-174 PDF
  • Kevin Grant, David Mould, Michael Horsch, Eric Neufeld, "Enhancing Demosaicking Algorithms using Loopy Propagation." In Proceedings of the Fifth International Conference on Computer Graphics and Artificial Intelligence. May 2003, Limoges (FRANCE), pp. 129-142 postscript
  • Vassileva, J. Breban, S. Horsch M. "Agent Reasoning Mechanism for Long-Term Coalitions Based on Decision Making and Trust." Computational Intelligence, 18, 4, 583-595, November 2002 Special Issue on Agent Mediated Electronic Commerce. PDF
  • Michael C. Horsch, William S. Havens, Aditya K. Ghose. "Generalized Arc Consistency with Application to MaxCSP and SCSP Instances." In Proceedings of the Fifteenth Canadian Conference on Artificial Intelligence, May 2002, Calgary, pp 104--118. PDF
  • Michael C. Horsch and William S. Havens. Probabilistic Arc Consistency: A connection between constraint reasoning and probabilistic reasoning. In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pages 282-290, 2000. compressed postscript
  • Michael C. Horsch and William S. Havens. An empirical evaluation of Probabilistic Arc Consistency as a variable ordering heuristic: Extended Abstract. In Sixth International Conference on Principles and Practice of Constraint Programming. compressed postscript
  • Michael C. Horsch and David Poole. Estimating the Value of Computation in Flexible Information Refinement. In Proceedings of the Fifteenth Conference in Uncertainty in Artificial Intelligence, 1999. compressed postscript
  • Michael C. Horsch and David Poole. An Anytime Algorithm for Decision Making under Uncertainty. In Proceedings of the Fourteenth Conference in Uncertainty in Artificial Intelligence, 1998. postscript
  • Michael C. Horsch and David Poole. Flexible Policy Construction by Information Refinement. In Proceedings of the Twelfth Conference in Uncertainty in Artificial Intelligence, 1996. postscript
  • Michael C. Horsch and David Poole. A dynamic approach to probabilistic inference using Bayesian networks. In Proceedings of the Sixth Conference in Uncertainty in Artificial Intelligence, 1990.
  • Michael C. Horsch. "Flexible Policy Construction by Information Refinement." PhD Thesis, University of British Columbia, 1998. postscript