Location: Bowdoin / Stephen M. Majercik

Computer Science

Stephen Majercik

Associate Professor of Computer Science, Chair of Department of Computer Science

Contact Information

smajerci@bowdoin.edu
207-725-3106
Computer Science

Searles Science Building - 222



Teaching this semester

CSCI 2200. Algorithms

An introductory course on the design and analysis of algorithms. Introduces a number of basic algorithms for a variety of problems such as searching, sorting, selection, and graph problems (e.g., spanning trees and shortest paths). Discusses analysis techniques, such as recurrences and amortization, as well as algorithm design paradigms such as divide-and-conquer, dynamic programming, and greedy algorithms.

CSCI 3445. Nature-Inspired Computation

The size and complexity of real-world optimization problems can make it difficult to find optimal solutions in an acceptable amount of time. Researchers have turned to nature for inspiration in developing techniques that can find high-quality solutions in a reasonable amount of time; the resulting algorithms have been applied successfully to a wide range of optimization problems. Covers the most widely used algorithms, exploring their natural inspiration, their structure and effectiveness, and applications. Topics drawn from: genetic algorithms, particle swarm optimization, ant colony optimization, honeybee algorithms, immune system algorithms, and bacteria optimization algorithms. Requirements include labs, programming assignments, and a larger final project.



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

Book Chapters 

Stephen M. Majercik.  Stochastic satisfiability.  In Handbook of Satisfiability, eds. Armin Biere, Marijn Heule, Hans van Maaren and Toby Walsch, pp. 887-925, IOS Press, 2009.

Journal Articles

Stephen M. Majercik.  Alternative Topologies for GREEN-PSO.  Invited article in Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2013, Vilamoura, Portugal, September 20-22, 2013, eds. K. Madani, A. D. Correia, A. Rosa, J. Filipe, and J. Kacprzyk, pp.155-171, Springer, 2016.

Stephen M. Majercik.  APPSSAT:  Approximate probabilistic planning using stochastic satisfiability.  Invited article in International Journal of Approximate Reasoning, 45(2):  pages 402-419, Elsevier Publishing, 2007.

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):  pages 119-162, Elsevier Publishing, 2003.

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

Peer-Reviewed Conference Proceedings

Frank Mauceri and Stephen M. Majercik.  A swarm environment for experimental performance and improvisation.   In Proceedings of EvoMUSART: The 6th International Conference on Computational Intelligence in Music, Sound, Art and Design, Lecture Notes in Computer Science, v. 10198, pp. 190-200, Springer, 2017.

Stephen M. Majercik.  Using Fluid Neural Networks to create dynamic neighborhood topologies in Particle Swarm Optimization.   In Proceedings of the Ninth International Conference on Swarm Intelligence, Lecture Notes in Computer Science, v. 8667, pp. 270-277, Springer, 2014.

Stephen M. Majercik.  GREEN-PSO: Conserving function evaluations in Particle Swarm Optimization.  In Proceedings of the Fifth International Conference on Evolutionary Computation Theory and Applications, pp. 160-167, 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, Lecture Notes in Computer Science, v. 7166, pp. 109-114, 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, pages 416-422, AAAI Press, 2005.

Stephen M. Majercik.  APPSSAT:  Approximate probabilistic planning using stochastic satisfiability.  In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lluis Godo, ed.,  Lecture Notes in Artificial Intelligence, eds. J.G. Carbonell and J. Siekmann, v. 3571, pages 209-220, Springer, 2005. Lecture Notes in Computer Science

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

Stephen 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, pages 163-172, AAAI Press, 2002. Artificial Intelligence Planning and Scheduling

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.  Contingent planning under uncertainty via stochastic satisfiability.  In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 549-556, AAAI Press/MIT Press, 1999.

Stephen M. Majercik.  Planning under uncertainty via stochastic satisfiability.  In Proceedings of the Sixteenth National Conference on Artificial Intelligence, page 950, AAAI Press/MIT Press, 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, AAAI Press/MIT Press, 1998.

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, AAAI Press, 1998.

Michael L. Littman and Stephen M. Majercik.  Large-scale planning under uncertainty:  A survey.  In NASA International Workshop on Planning and Scheduling for Space Exploration and Science, pages 27: 1-8, 1997.

Other 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.  Ph.D. thesis, Department of Computer Science, Duke University, September 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 (held at the Fifth International Conference on Artificial Intelligence Planning and Scheduling), pages 48-54, 2000.

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

Stephen M. Majercik and Michael L. Littman.  MAXPLAN:  A new approach to probabilistic planning.  In Proceedings of the 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 (held at the Fourth International Conference on Artificial Intelligence Planning Systems), pages 85-88, 1998. 

Stephen M. Majercik and Michael L. Littman.  Reinforcement learning for selfish load balancing in a distributed memory environment.  In Proceedings of the International Conference of Information Sciences, Paul P. Wang, ed., v. 2, pages 262-265, 1997.

Stephen M. Majercik.  Structurally dynamic cellular automata.  Master's thesis, Department of Computer Science, University of Southern Maine, August 1994.

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