Several Papers on Reasoning and Representation under Limited Computational Resources

• E. Horvitz. Principles and Applications of Continual Computation. Artificial Intelligence Journal, February 2001.
  
  
• E. Horvitz, Y. Ruan, C. Gomes, H. Kautz, B. Selman, D. M. Chickering. A Bayesian Approach to Tackling Hard Computational Problems. Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, August 2001.
  
  
• H. Kautz, E. Horvitz, Y. Ruan, C. Gomes, B. Selman. Dynamic Restart Policies. Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, Alberta, July 2002. AAAI Press.
  
  
• E.J. Horvitz, Rational Metareasoning and Compilation for Optimizing Decisions Under Bounded Resources. Proceedings of Computational Intelligence '89, Milan, Italy, September 1989. Association for Computing Machinery.
  
  
• E. Horvitz and A. Klein. Studies of Theorem Proving under Limited Resources. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, August 1995.
  
  
• E.J. Horvitz, G.F. Cooper, D.E. Heckerman, Reflection and action under scarce resources: Theoretical principles and empirical study. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI. August 1989, pp. 1121-1127. International Joint Conference on Artificial Intelligence.
  
  
• E.J. Horvitz, Reasoning under varying and uncertain resource constraints. Proceedings of the Seventh National Conference on Artificial Intelligence, Minneapolis, MN. August 1988. pp, 111-116. Morgan Kaufmann, San Mateo, CA.
  
  
• E. Horvitz, Reasoning about Beliefs and Actions under Computational Resource Constraints, Proceedings of the Third Conference on Uncertainty in Artificial Intelligence, Seattle, Washington. July 1987. Association for Uncertainty and Artificial Intelligence. pp. 429-444. Also in L. Kanal, et al. ed., Uncertainty in Artificial Intelligence 3, Elsevier, 1989, pp. 301-324.
  
  
• E. Horvitz. Computation and Action under Bounded Resources. PhD Dissertation, Stanford University, 1990.
  
  
• J.S. Breese and E.J. Horvitz. Ideal Reformulation of Belief Networks: A Study in Bounded Optimality, Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence, Cambridge, MA, Association for Uncertainty in Artificial Intelligence, Mountain View, CA.
  
  
• D. Heckerman, J.S. Breese, E. Horvitz, The Compilation of Decision Models, Proceedings of the Conference on Uncertainty in Artificial Intelligence, Association for Uncertainty in Artificial Intelligence, July 1989, pages 162-173.
  
  
• E.J. Horvitz and J.S. Breese, Ideal Partition of Resources for Metareasoning. Stanford University Computer Science Department Technical Report KSL-90-26, February 1990.
  
  
• E.J. Horvitz, H.J. Suermondt, G.F. Cooper. Bounded conditioning: Flexible inference for decisions under scarce resources. In: Proceedings of Conference on Uncertainty in Artificial Intelligence, Windsor, ON. August 1989, pp. 182-193. Association for Uncertainty in Artificial Intelligence, Mountain View, CA.
  
  
• E. Horvitz and G. Rutledge. Time-Dependent Utility and Action Under Uncertainty. Uncertainty in Artificial Intelligence, Los Angeles, pp. 151-158. Morgan Kaufman, 1991.
  
  
• Y. Ruan, E. Horvitz, H. Kautz, Restart Policies with Dependence among Runs: A Dynamic Programming Approach, In Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming, September 2002, Ithaca, New York.
  
  
• Y. Ruan, H. Kautz, E. Horvitz, The Backdoor Key: A Path to Understanding Problem Hardness, Proceedings of the Nineteenth National Conference on Artificial Intelligence, AAAI 2004, San Jose, CA..
  
  
• E. Horvitz. Thinking Ahead: Continual Computation Policies for Allocating Offline and Real-Time Resources. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI '99, July 1999, pp. 1280-1286.
  
  
• E. Horvitz. Continual Computation Policies for Utility-Directed Prefetching. Proceedings of the Seventh ACM Conference on Information and Knowledge Management, November 1998, pp. 175-184.
  
  
• E. Horvitz and J. Lengyel. Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, August 1997.
  
  
• E. Horvitz and A. Seiver. Time-Critical Action: Representations and Application. Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, August 1997.
  
  
• E. Horvitz. Models of Continual Computation. Proceedings of the Fourteenth National Conference on Artificial Intelligence, July 1997.
  
  
• E. Horvitz and S. Zilberstein, Computational Tradeoffs Under Bounded Resources, Artificial Intelligence Journal, 126:1-4, Elsevier Science, February 2001.
  
  
• B. Selman, R. Brooks, T. Dean, E. Horvitz, T. Mitchell, N. Nilsson. Challenge Problems for Artificial Intelligence. Proceedings of AAAI-96, Thirteenth National Conference on Artificial Intelligence, Portland, Oregon, August 1996. AAAI Press, Menlo Park, California, pp. 1340-1345.