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Gettysburg College
300 N. Washington St.
Dept. of Computer Science
Campus Box 402
Gettysburg, PA 17325

Phone: 717-337-6630
Fax: 717-337-6638
 
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CONTEXT
Todd W. Neller
Computer Science Dept.
Gettysburg College
Gettysburg
Pennsylvania
USA

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Professor of Computer Science at Gettysburg College

COURSES
  • (no current courses)

Previous courses: CS 103: Introduction to Computing (Home Page, Online Text), CS 107: Introduction to Scientific Computation, CS 111: Computer Science I, CS 112: Computer Science II, CS 216: Data Structures, CS 341: Survey of Programming Languages, CS 371: Artificial Intelligence, CS 374: Compilers, CS 391: Selected Topics: Data Mining, CS 391: Selected Topics: Game Artificial Intelligence, CS 391: Selected Topics: Machine Learning, CS 392: Selected Topics: Game Artificial Intelligence.
 

RESOURCES
GAMES AND PUZZLES
ABOUT

Todd W. Neller is a Professor of Computer Science at Gettysburg College. A Cornell University Merrill Presidential Scholar, he received a B.S. in Computer Science with distinction in 1993. In 2000, he received his Ph.D. with distinction in teaching at Stanford University, where he was awarded a Stanford University Lieberman Fellowship, and the George E. Forsythe Memorial Award for excellence in teaching. His dissertation concerned extensions of artificial intelligence (AI) search algorithms to hybrid dynamical systems, and the refutation of hybrid system properties through simulation and information-based optimization. A game enthusiast, Neller has in recent years enjoyed pursuing game AI challenges, computing optimal play for jeopardy dice games such as Pass the Pigs and bluffing dice games such as Dudo, creating new reasoning algorithms for Clue/Cluedo, analyzing optimal Risk attack and defense policies, and designing logic mazes.
 

ROOK JUMPING MAZES
ROOK JUMPING MAZE GENERATION: What is a Rook Jumping Maze, and how can they be generated? Enjoy the fun results of our faculty and student design project.


REINFORCEMENT LEARNING
REINFORCEMENT LEARNING (RL) techniques allow computers to learn good behaviors through trial and error rather than have such behaviors explicitly programmed.

We apply such techniques to the metalevel control of search and optimization algorithms.  For example, we have successfully applied RL techniques to the control of simulated annealing, dynamically adjusting the temperature and deciding when to terminate optimization.