CS 4701 – Practicum in Artificial Intelligence Pre-proposal Presentation TEAM SKYNET: Brian Nader Stephen Stinson Rei Suzuki

Problem Statement and Motivation Write a Chinese checkers AI Improve our understanding of… Heuristic Search Machine Learning Other AI features Motivation: TO TERMINATE ALL ENEMIES!

Input/Output Server  AI Input 1 st input: initial board state Opponent piece movements Illegal move errors Game end condition AI Output  Server Movement requests

Background Reading Main Reading: Artificial Intelligence: A Modern Approach, Russell and Norvig, Prentice-Hall, Inc. 3 rd Ed. Secondary Sources include, but are not limited to: The Internet

General Approach Create a modular AI that makes use of: Heuristic search functions Multiple heuristics and heuristic combinations Hard-coded strategies (opening moves) Unique strategies

System Architecture && Work Plan Heuristic function class Game state saved at each node Search algorithm class Neutral peg placement function Main([args]) class

Data Sources Chinese checkers statistics available online/reference material Nightly testing results

Evaluation Plans AI vs. AI games If simulator is provided Nightly test results Wins/Losses Any other information provided

Schedule Week 1 (9/11—9/17): Research, proposal completion Week 2: Design core structure of code base / begin coding Week 3: Finish coding core structure/ finalize decisions on heuristic and algorithm choices Week 4: Have prototype AI fully functional (able to move pieces)/ continue developing algorithms Code Review : Tuesday October 4 th Week 5: Begin testing/optimizing heuristics and searches / searches should be finished Week 6: Development of unique strategies and ensuring our AI is competitive Week 7: Testing and finalization of unique strategies Week 8: Finalize project Code Review: Tuesday November 1 st Weeks 9-12: Writing final reports and tweaking AI Final Project Due: November 29th (Judgment Day)

Questions for you! Neutral Pegs Does the peg stay there for the remainder of the game? How many? How will the AI know? Cross-positions? Simulator? Memory constraints Query the server? Nightly Testing results How much info will be provided from the tests? Board Shape Holes? Will the board change from testing to competition?