Game playing is one of the most studied areas of artificial intelligence. Game AI techniques produce intelligent behavior in video game characters. These techniques include pathfinding, decision making, planning, and developing agents that can match or exceed human performance. Game playing provides a test bed for AI ideas because games have well-defined rules and provide challenges in developing rational agents.

1. Game playing
 GAMES ARE FUN! .THEY ARE LIMITED, WELL-DEFINED RULES .THEY PROVIDE
ADVANCED, EXISTING TEST-BEDS FOR DEVELOPING SEVERAL IDEAS AND
TECHNIQUES THAT ARE USEFUL ELSEWHERE.

2. Area of interest
 Definitions
 History, Overview
 Concepts
 Applications
 Current trends in this field
 One of the most studied and
most interesting areas of
Artificial Intelligence is game
playing. They are Fun, and
HARD to create

3. DEFINITION
 Game artificial intelligence refers to techniques used in computer and video
games to produce the illusion of intelligence in the behavior of non-player
characters (NPCs)
 Hacks and cheats are acceptable and, in many cases, the computer abilities must
be toned down to give human players a sense of fairness. E.g racing and shooting
 Emphasis of game AI is on developing rational agents to match or exceed human
performance

4. Definition cont …
 AI has continued to improve, with aims set on a player being unable to tell the
difference between computer and human players - remember Turin test?
 A game must ‘feel’ natural
 Obey laws of the game
 Characters aware of the environment
 Path finding (A* algorithm)
 Decision making
 Planning
 Game ‘bookkeeping’, scoring
 ~50% of game project time is spent on building AI

5. Why are games relevant to AI?
 Games are fun!
 They are limited, well-defined rules
 They provide advanced, existing test-beds for developing several ideas and techniques that are useful
elsewhere.
 They are one of the few domains that allow us to build agents.
 Studying games teaches us how to deal with other agents trying to foil our plans
 Huge state spaces – Games are highly complex! Usually, there is not enough time to work out the
perfect move. E.g Go & Chess
 Nice, clean environment with clear criteria for success
 Game playing is considered an intelligent human activity.
 AI has always been interested in abstract games.
 Games present an ideal environment where hostile agents may compete

6. State space
 Coarser-grain than Tactical AI (Very Large space search)
 Problem solving
 Goal formulation
 Problem formulation
 Search
 Solution
 Execution

7. The Illusion of Human Behaviour
 Game AI is about the illusion of human behaviour
 Smart, to a certain extent (Creativity)
 Non-repeating behaviour
 Unpredictable but rational decisions
 Emotional influences (Irrationality, ‘Personality’)
 Body language to communicate emotions
 Being integrated in the environment
 Game AI needs various computer science disciplines
 Knowledge Based Systems
 Machine Learning
 Multi-agent Systems
 Computer Graphics & Animation
 Data Structures

8. Computer Games Types
 Strategy Games
 Real-Time Strategy (RTS)
 Turn-Based Strategy (TBS)
 Helicopter view
 Role-Playing Games (RPG)
 Single-Player
 Multi-Player (MMORPG)
 Action Games
 First-Person Shooters (FPS)
 First-Person Sneakers
 Sports Games
 Simulations
 Adventure Games
 Puzzle Games

9. History and overview
 Minimax - Developed by John von Neumann in 1928 - This algorithm is used
extensively in game theory
 Samuel’s learning program (1959) - The program learns through the manipulation
of the summation of heuristics. - If the program wins, it raises high heuristic values and
lowers low ones. If it loses, it does the opposite.
 1960s - Progress and success in Game AI. - Creating a successful AI meant coming up
with the right rules for it to follow. 1970s-1980s - Transition to games as entertainment
- Using search based AI to emulate entertaining characters would be unnatural and
clumsy - Game play is based more on skill than on rules
 Early 1990s - Increased realism becomes the primary focus of the game industry - A
rift develops between the developers of popular games and AI researchers

10. CONCEPTS Technologies / techniques
used
 Machine learning
 Waypoint Graph
 Continuous Control
 Cellular Automata
 Case Based Reasoning
 Blend Database
 Dynamic Programming
 Finite State Machines
 Fuzzy Logic
 Influence Mapping
 Motion Graph
 Motion Capture
 Navigation Mesh
 Navigation Graph
 Artificial Neural Networks
 Policy Search
 Path Planning
 Path Following
 Steering
 Alpha-Beta pruning
 MiniMax

11. Game setup
 Two players: A and B
 A moves first and they take turns until the game is over. Winner gets award, loser gets
penalty.
 Games as search:
 Initial state: e.g. board configuration of chess
 Successor function: list of (move, state) pairs specifying legal moves.
 Terminal test: Is the game finished?
 Utility function: Gives numerical value of terminal states. E.g. win (+1), lose (-1) and draw (0)
in tic-tac-toe
 A uses search tree to determine next move.

12. How to Play a Game by Searching
 General Scheme
-Consider all legal moves, each of which will lead to some new state of the
environment (‘board position’)
 Evaluate each possible resulting board position
 Pick the move which leads to the best board position.
 Wait for your opponent’s move, then repeat.
 Key problems
 Representing the ‘board’
 Representing legal next boards
 Evaluating positions
 Looking ahead

13. Applications
 Training Simulators
 Effective training often requires thousands of people
 Computer generated stand-ins can cut costs
 Military simulations
 Management simulations
 Economic simulations
 Education
 Software for pre-school children
 Entertainment
 Virtual Environments
 Simulated worlds allows AI researchers to concentrate on algorithms instead of sensors
 Again, computer generated robots are cheaper than actual robots

14. Applications
 Human-level AI
 Every person is an expert on human-level intelligence
 Testing of AI is easy
 Movies
 Crowd scenes
 Flocking behavior
 Realistic Movement

15. Game AI Major Challenges
 Resources - Realism in computer games focused on graphics - Advanced
graphics requires many CPU cycles - Recent advances in computer hardware have
someone alleviated this issue.
 Deadlines - Game engine must be developed before AI can be tested - AI
programmers often have to compromise to meet deadlines
 Over Intelligence - Perfect AI would be easier to code - It would lack
believability and not be fun. - Human-level AI should quit, surrender, or run away-
even fight to the end.
 Research and Development - Lack of cohesion between AI research community
and game developers - AI in modern computer games seems trivial to AI
researchers

16. Summary
 Games are to AI as grand prix racing is to automobile design Games are fun to
work on (and dangerous)
 They illustrate several important points about AI
 perfection is unattainable, must approximate
 it is a good idea to think about what to think about
 uncertainty constrains the assignment of values to states

17. References
 Artificial Intelligence: A Modern Approach' (Second Edition) by Stuart Russell and
Peter Norvig, Prentice Hall Pub.
 http://www.cs.umbc.edu/471/notes/pdf/games.pdf
 http://l3d.cs.colorado.edu/courses/AI-96/sept23glecture.pdf
 Theodore L. Turocy, Texas A&M University, Bernhard von Stengel, London School
of Economics "Game Theory" CDAM Research Report Oct. 2001
 http://www.uni-koblenz.de/~beckert/Lehre/Einfuehrung-KI-SS2003/folien06.pdf
[6] http://ai-depot.com/LogicGames/MiniMax.html