Associate Professor and Chair of Computer Science at Gettysburg College

• CS 111: Computer Science I
• CS 216: Data Structures

Previous courses: CS 103: Introduction to Computing (Home Page, Online Text), CS 107: Introduction to Scientific Computation, CS 112: Computer Science II, CS 341: 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.
 

• Model AI Assignments
• Publications
• Talks
• Teaching Resources: K-12, Pig (CS1, AI), Clue, search, stochastic local search, MLExAI

• The Game of Pig
• Mazes: Rook Jumping Maze, Word Maze Puzzle, Oskar's 4-Bit Maze
• Peg Squirrelitaire
• Game Resources and Recommendations, Chess and Chip Games
• Stores: Funagain, GameSurplus, BoardGamePrices
• Sites: BoardGameGeek, Recommended Games for Children
• Paper Pen-guins, Decorative Post-it Wall Tetris, Interactive Fiction, Poker Squares

Todd W. Neller is an Associate Professor and Chair 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.
 

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.