CSE 7102, Spring 2006: Research Seminar on Artificial Intelligence

Instructor:  Yixin Chen
Seminar Time and Place:  Fridays 1:30pm - 2:30pm at Eads 216
Description:  This seminar is intended for students interested in conducting research in the field of artificial intelligence. Each semester is devoted to an in-depth study of one topic, primarily by detailed reading of current research papers.

Schedule and Conent
Date Presenter
paper #
 Presentation title Topic category
02/10 Zhao Xing
1, 2
Planning as Satisfiability and SAT solvers Constraint satisfaction
02/17 Sharlee Climer
21
   Integer/mixed-integer optimization
02/24 Vanessa Clark
 
   IMRT optimization
02/24 Qian Wan
5
   Combinatorial search
03/03 Ruoyun Huang
6, 7
   Planning and Scheduling
03/10
 Thomas E. Shepherd
8
   Planning and Scheduling
03/24
 Andrew Levine
9, 10
   Markov decision process(MDP), POMDP
04/07
 Robert Glaubius
Nuzhet Atay
11
12
   Markov decision process(MDP), POMDP
04/14
 Gazihan Alankus
13, 14
   Data classification
04/21
 Nathan Jacobs
15, 16
   Hidden Markov models
04/28
 Ben Delaware
18
   Networking modeling and optimization

List of Papers
  1. Henry Kautz, David McAllester, and Bart Selman , Encoding Plans in Propositional Logic , Proc. KR-96.
  2. Henry Kautz and Bart Selman , Planning as Satisfiability , Proceedings ECAI-92.
  3. ML Fisher , The Lagrangian Relaxation Method for Solving Integer Programming Problems , Management Science, (1981)
  4. G. Dantzig, R. Fulkerson, and S. Johnson , Solution of a Large-Scale Traveling-Salesman Problem , Operations Research 2 (1954), 393.
  5. M. Held and R. Karp , The Traveling-Salesman Problem and Minimum Spanning Trees , Operations Research 18 (1970), 1138.
  6. Menkes van den Briel, Thomas Vossen and Subbarao Kambhampati , Reviving Integer Programming Approaches for AI Planning: A Branch-and-Cut Framework , ICAPS 2005.
  7. Malte Helmert and Silvia Richter , Fast Downward Making use of causal dependencies in the problem representation , IPC 2004.
  8. D. Smith, J. Frank, and A. J'onsson , Bridging the gap between planning and scheduling , Knowledge Engineering Review, 15(1):61--94, 2000.
  9. B. Givan and R. Par , An Introduction to Markov Decision Processes , MDP Tutorial.
  10. D.S. Bernstein, S. Zilberstein, and N. Immerman , The Complexity of Decentralized Control of Markov Decision Processes , Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Stanford, California, July, 2000.
  11. Z. Feng and S. Zilberstein , Region-Based Incremental Pruning for POMDPs , Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada. 2004.
  12. Z. Feng and S. Zilberstein , Efficient Maximization in Solving POMDPs , Proceedings of the Twentieth National Conference on Artificial Intelligence, Pittsburgh, Pennsylvania, 2005.
  13. A. Moore , A tutorial on Bayesian networks , 2001.
  14. N. Zhang , Hierarchical Latent Class Models for Cluster Analysis , Journal of Machine Learning Research.
  15. Lawrence R. Rabiner , A tutorial on hidden markov models and selected applications in speech recognition , 1989.
  16. John Lafferty, Andrew McCallum and Fernando Pereira , Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data .
  17. Aron Culotta, David Kulp and Andrew McCallum , Gene Prediction with Conditional Random Fields . Technical Report UM-CS-2005-028. University of Massachusetts, Amherst, 2005.
  18. S. H. Low , A Duality Model of TCP and Queue Management Algorithms . ITC Specialist Seminar on IP Traffic Measurement, Modeling and Management, September 18-20, 2000, Monterey, CA. Updated version appears in IEEE/ACM Trans. on Networking, 11(4):525-536, August 2003.
  19. S. H. Low and D. E. Lapsley , Optimization Flow Control, I: Basic Algorithm and Convergence . IEEE/ACM Transactions on Networking, 7(6):861-75, Dec. 1999.
  20. Frank Kelly , Mathematical modelling of the Internet Mathematics Unlimited - 2001 and Beyond . editors B. Engquist and W. Schmid. Springer-Verlag, Berlin, 2001.
  21. Sharlee Climer and Weixiong Zhang, Cut-and-Solve: An Iterative Search Strategy for Combinatorial Optimization Problems, Artificial Intelligence, 2006.