Location: Bowdoin / Stephen M. Majercik

Computer Science

Stephen Majercik

Associate Professor of Computer Science

Contact Information

smajerci@bowdoin.edu
207-725-3106
Computer Science
222 Searles Science Building



Spring 2014

  • Introduction to Computer Science (CSCI 1101A)
  • Optimization and Uncertainty (CSCI 3425)


Stephen M. Majercik- Bowdoin College - Computer Science Department

Education

  • Ph.D. in Computer Science, Duke University, 2000.
  • M.S. in Computer Science, University of Southern Maine, 1994.
  • M.B.A. in Finance, Yale School of Management, 1981.
  • M.F.A. in Theatre Administration, Yale School of Drama, 1981.
  • A.B. cum laude in Government, Harvard University, 1977

Research Interests

  • Nature inspired computation:
          Swarm intelligence
          Particle Swarm Optimization
  • Computation and the arts:
          Uses of artificial intelligence techniques in the arts
          Using technology as an expressive medium
  • Stochastic satisfiability:
          Efficient solution techniques

Links





Refereed Publications

Stephen M. Majercik.  GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization.  To appear in the Proceedings of the Fifth International Conference on Evolutionary Computation Theory and Applications, 2013.
 
Stephen M. Majercik.  Initial experiments in using communication swarms to improve the performance of swarm systems.  In Proceedings of the Sixth International Workshop on Self-Organizing Systems, pp. 109-114, LNCS 7166, Springer, 2012.
 
William K. Richard and Stephen M. Majercik.  Swarm-based path creation in dynamic environments for search and rescue.  In Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation, pp. 1401-1402, ACM, 2012.

Stephen M. Majercik and Byron Boots. DC-SSAT: A Divide-and-Conquer Approach to Solving Stochastic Satisfiability Problems Efficiently. In Proceedings of the Twentieth National Conference on Artificial Intelligence, 416-422. The AAAI Press, 2005.
PDF of Conference Program »

Abstract »

 Lecture Notes in Computer ScienceStephen M. Majercik. APPSSAT: Approximate Probabilistic Planning Using Stochastic Satisfiability. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lluis Godo, ed., Lecture Notes in Computer Science, eds. J.G. Carbonell and J. Siekmann, v. 3571, 209-220. Springer, 2005.
Abstract »

Proceedings of the Sixteenth International Conference on Tools With Artificial IntelligenceStephen M. Majercik. Nonchronological backtracking in stochastic Boolean satisfiability. In Proceedings of the Sixteenth International Conference on Tools With Artificial Intelligence, 498-507. IEEE Press, 2004

Stephen M. Majercik and Michael L. Littman. Contingent planning under uncertainty via stochastic satisfiability. Artificial Intelligence Journal Special Issue on Planning With Uncertainty and Incomplete Information, 147(1-2):119-162, 2003.
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Proceedings of the Sixth International Conference on Artificial Intelligence Planning and SchedulingStephen M. Majercik and Andrew P. Rusczek. Faster probabilistic planning through more efficient stochastic satisfiability problem encodings. In Proceedings of the Sixth International Conference on Artificial Intelligence Planning and Scheduling, 163-172, AAAI Press, 2002.

Michael L. Littman, Stephen M. Majercik, and Toniann Pitassi, Stochastic Boolean satisfiability. Journal of Automated Reasoning, 27(3):251-296, 2001. (abstract, postscript).

Stephen M. Majercik and Michael L. Littman. Contingent planning under uncertainty via stochastic satisfiability. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 549-556, 1999. (abstract, postscript).

Stephen M. Majercik. Planning under uncertainty via stochastic satisfiability. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, page 950, 1999. Presented at the SIGART/AAAI-99 Doctoral Consortium.

Stephen M. Majercik and Michael L. Littman. Using caching to solve larger probabilistic planning problems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 954-959, 1998. (abstract, postscript).

Stephen M. Majercik and Michael L. Littman. MAXPLAN: A new approach to probabilistic planning. In Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems, pages 86-93, 1998. (abstract, postscript).

Michael L. Littman and Stephen M. Majercik. Large-Scale Planning Under Uncertainty: A Survey. In International Workshop on Planning and Scheduling for Space Exploration and Science, pages 27:1-8, 1997. (postscript)

Unrefereed Publications

Stephen M. Majercik. APROPOS2: Approximate probabilistic planning out of stochastic satisfiability. In Papers from the AAAI Workshop on Probabilistic Approaches in Search (held at the Eighteenth National Conference on Artificial Intelligence, pages 29-34. AAAI Press, 2002.

Stephen M. Majercik. Planning under uncertainty via stochastic satisfiability. In Proceedings of the AAAI Fall Symposium on Using Uncertainty Within Computation, pages 83-84, 2001.

Stephen Michael Majercik. Planning Under Uncertainty via Stochastic Satisfiability. PhD thesis, Department of Computer Science, Duke University, September 2000.

Stephen M. Majercik and Michael L. Littman. Approximate planning in the probabilistic-planning-as-stochastic-satisfiability paradigm. In Second NASA International Workshop on Planning and Scheduling for Space, pages 60-66, 2000.

Stephen M. Majercik and Michael L. Littman. ZANDER: A model-theoretic approach to planning in partially observable stochastic domains. In Working Notes of the Workshop on Model-Theoretic Planning, pages 48-54, Breckenridge, CO, 2000. Held in conjunction with AIPS-2000.

Stephen M. Majercik. C-MAXPLAN: Contingent planning in the MAXPLAN framework. In AAAI Spring Symposium on Search Techniques for Problem Solving Under Uncertainty and Incomplete Information, pages 83-88, 1999. (abstract, postscript).

Stephen M. Majercik and Michael L. Littman. MAXPLAN: A new approach to probabilistic planning. In AAAI Fall Symposium on Planning with Partially Observable Markov Decision Processes, pages 121-128, 1998.

Stephen M. Majercik and Michael L. Littman. Probabilistic Planning with MAXPLAN. In Working Notes of the Workshop on Planning as Combinatorial Search, pages 85-88, 1998. Held in conjunction with AIPS-98.

Stephen M. Majercik and Michael L. Littman. Reinforcement learning for selfish load balancing in a distributed memory environment. In Proceedings of the International Conference on Information Sciences, volume 2, Paul Wang editor, pages 262-265, 1997. (abstract, postscript)

Stephen M. Majercik. Structurally dynamic cellular automata. Master's Thesis, Department of Computer Science, University of Southern Maine, 1994. (abstract, postscript)

Curriculum vitae in PDF formPDF »