Global Domination -RISK-

1. Global Domination-RISK-

2. Table of Contents • Introduction • Type Of Game • Formats • Game Play • Artificial Intelligence • Type of A.I. • Variations of the A.I. • Improvements to the A.I.

3. Introduction • Type of game • History of the game • Platforms

4. Type of game • Turn based strategy game • Multiple computer players available • Choices range 2 – 6 players • Multiple variations of the rules • Set number of troops for “turning in a book” • Increasing number of troops for “turning in a book”

5. History • Created by Hasbro • Originally a board game • First computer version was available with Commodore 64, and IBM compatible machines • B.P. (Before Pentium) • Many different versions exist for marketability (Lord of the Rings, Star Wars, etc…)

6. Platforms • PC • 400mhz+ for good game play • Windows ’95 or greater • Slight problems with Windows XP • Mac • Play Station 2 • Evaluation Version: PC

7. Game Play • Initial number of troops to set on a random deal of countries • Each turn you get reinforcements based on the number of countries you control, and the continents you control • Cards are given at the end of the turn if a successful conquest took place • Cards will have a cannon, Knight, or soldier on it. • A book can be three of a kind, or one of each • Goal is to: • Overpower opponents • Defeat them from the game • Conquer the world

10. Artificial Intelligence • Risk utilizes prediction algorithms and battle strategies • Actual algorithms were not disclosed • Popular heuristic algorithm were more than likely used within a powerful rule based system for the battle strategy • Rule Based systems often use human experts as guides

11. Different Algorithms • Exhaustive Search • Good for simple games (Tic-Tac-Toe) • More than likely not used for Risked based on the number of combinations and decisions to take into account

12. Improved Searches • Main goal of a prediction algorithm is to make an informed search to locate as few open states as possible based on the search diagram • Such Heuristic searches can be: • Best-First (hill climbing) • A* • Minimax

13. Best First Search • Expand the current node • Evaluate its children • Moves onto best child for further expansion

14. A* • If algorithm A is used with an evaluation function in which h(n) <= to the cost of the minimal path from n to the goal • Given a search function h(n) • Known starting point and goal • A* will produce the shortest path • For game design, these parameters may not be known

15. Minimax • Assumes that the opponent or opponents use the same knowledge of the play space, and uses that knowledge to win the game • Opponents are referred to as Min and Max • Max represents player trying to win • Min is the opponent trying to minimize Max’s score

16. Minimax cont… • Most logical course of action for any strategy game • When incorporated into a battle strategy system • Can predict to a point a player’s next move. • Improvements to the searching algorithm and other variances can create a more difficult A.I.

17. Battle Strategies • Rule based • Situation Based • Game of this level would have an extensive system

18. Improvements • Improving both the search algorithms and case bases • Results in different levels of A.I. • Results in a superior game with each new release

19. This version • By allowing the user to change the way the over all game is played • Adjusts the variables for both the heuristic searches and battle strategies