Professor of Computer Science at Gettysburg College

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 374: Compilers, CS 391: Selected Topics: Kaggle Competition, CS 391: Selected Topics: Game Artificial Intelligence, CS 391: Selected Topics: Data Mining, CS 391: Selected Topics: Machine Learning, CS 392: Selected Topics: Game Artificial Intelligence, DS 256: Data Science Programming, FYS 187-4: Games and Computation.
 

• Model AI Assignments and our EAAI Mentored Undergraduate Research Challenge: AI for Accessibility in Communication (past MURCs: 2023, 2022, 2021, 2019, 2017, 2015)
• Publications
• Talks
• Teaching Resources: K-12, Pig (CS1, AI), Clue, search, stochastic local search, MLExAI, counterfactual regret minimization (CFR), Getting Things Done
• Free Machine Learning Books

• The Game of Pig, FairKalah, Red Light 
• Mazes: Birds of a Feather, Rook Jumping Maze, Word Maze Puzzle, Oskar's 4-Bit Maze
• Game Resources and Recommendations, Chess and Chip Games, Recommended Games for Children
• Paper Pen-guins, Decorative Post-it Wall Tetris, Interactive Fiction, Poker Squares, Parameterized Poker Squares, Gin Rummy, AI-Assisted Game Design, Soma Cube Puzzle Resources
• Peg Squirrelitaire

Todd W. Neller is a Professor of Computer Science at Gettysburg College, and was the recipient of the 2018 AAAI/EAAI Outstanding Educator Award.  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 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 games and puzzles.
 

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.