Stephen M. Majercik
Associate Professor of Computer Science, Chair of Computer Science Department
Spring 2009
- Introduction to Computer Science (CSCI 101)
- Introduction to Computer Science (CSCI 101L1)
- Artificial Intelligence and Computer Games (CSCI 380)
| Phone | (207) 725-3106 |
| Title | Associate Professor |
| Department | COMPUTER SCIENCE |
| 2nd Title | Chair |
| 2nd Department | COMPUTER SCIENCE |
| Work Location | 222 Searles Science Building |
| smajerci@bowdoin.edu |
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
Links
Professor Majercik's personal web page »
Summer Research at Bowdoin: Artificial Intelligence Researcher Revels in the Impossible
Research Interests
- planning and reasoning under uncertainty
- scheduling under uncertainty
- human-computer collaborative planning
- model-theoretic planning
- decision-theoretic planning
- satisfiability and stochastic satisfiability
- constraint satisfaction programming
- Markov decision processes and partially observable Markov decision processes
- reinforcement learning
- belief network
Publications
Refereed Publications
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 »
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 Computer Science, eds. J.G. Carbonell and J. Siekmann, v. 3571, 209-220. Springer, 2005.
Abstract »
Stephen 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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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, 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)
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