it gives a basic information about how tha ai is sued for game development

1
Artificial Intelligence in Games
KAKINADA INSTITUTE OF TECHNOLOGICAL
SCIENCES RAMACHANDRAPURAM.
V.SHANMUKH
2

• What is AI.

• Computer Games.

• Computer Game Types.

• AI importance.

• Goals History of Game AI.

• problems in designing.

• game types.

• Popular AI Algorithms Used In Games.

3

• Artificial intelligence (AI) is the intelligence
  of machines and the branch of computer science
  that aims to create it.
• All knowledge based systems are developed by
  using Artificial intelligence.
• it was started with mcculloch pittes neuron model
  in the year of 1943.
• It developed by Alan Turing in 1950 with his
  Turings theory of computation i.e. Turing test.

4

• Game interaction between human player(s) and
  computer player(s).
• Even in multiplayers games.
• Keep the game challenging and enjoyable.

5
(No Transcript)
6

• Strategy games
• Role Playing Games (RPG)
• Action games
• First person shooter (FPS)
• Sports games

7

• AI Techniques used in computer and video games to
  produce the illusion of intelligence in the
  behavior of non-player characters.
• Found in most computer games is no AI but rather
  a mixture of techniques which are - although
  related to AI.
• At first CPU budgets for AI exploded and a
  number of games spent up to 30 of their
  processor time doing AI calculations but this
  has now leveled off at about 10 of CPU time.

8

• Be fun
• Reasonable challenge with natural behavior
• No Cheating!
• Having more health
• Driving faster
• Used to increase difficulty
• Run fast
• Use minimal memory

9

• 1960s
• First computer games (SpaceWar)
• Board games against the computer (Chess)
• 1970s
• Atari (1972)
• First AI implemented into games
• Space Invaders (1978)
• Distinct moving patterns
• Galaxian (1979)
• More complex and varied enemy
• movements

1-2 of CPU time spent on AI
10

• 1980s
• Fighting games
• Karate Champ (1984)
• AI defeated a human player in chess for the first
  time (1983)
• Pac-Man (1980)
• 1990s
• Finite state machines
• Deep Blue chess champ Gary (1997)
• Chess playing computer developed by IBM
• Graphic cards allowing for more CPU time
• 10-35 of CPU time spent on AI

11

• 2000s
• More games using neural networks
• Black White (2001)
• Collin McRae Rally 2 (2001)
• Hyperthreading
• More sophisticated AI engines while
  simultaneously creating a more realistic 3D
  environment

12

• Difficult task
• - Players wont like a game they cant win.
• - Players wont like a game they win
  easily.
• The most common problems found in modern computer
  games to which the intelligent actions of Naps
  are restricted to are
• path finding / path planning
• decision making
• steering / motion control

13
(No Transcript)
14

• Layered AI Structure
• Bottom layers trivial
• Determine paths
• Top layers non-trivial
• Reasoning and behavior
• Event Driven Engine
• Action based on events
• Good Idea to use leaking buckets to make flexible
• Leaking Buckets

15
(No Transcript)
16

• Path-Finding
• Handle collisions
• A()
• Event Driven Engine
• Maps Represented by a Rectangular Grid

17

• Interaction between event driven and goal driven
  engine example
• The goal based engine responds by moving units
  that are capable of air defense into position.

18
(No Transcript)
19

• Random encounters
• More common in games where fighting and gaining
  levels is more important
• Scripted behavior
• Often coupled with some minor AI
• Common in games depending more on their story
  line than other things
• Often a combination of both

20
(No Transcript)
21

• Cheating
• Racing games
• Segmentation
• Track gets split into small sectors.
• Each element gets its length calculated
• Optimization
• First represents the optimal driving track
• Second represents the track used when overtaking
  opponents

22

• A()
• Finite State Machines
• Artificial Neural Networks

23

• Goal Find shortest path
• Method to estimate distance between points
  (heuristic)
• Basic Method
• Try all paths
• Takes time
• Orient search towards target
• Minimizes areas of the map to be examined
• Uses heuristics that indicate the estimated cost
  of getting to the destination

24

• Example
• Manhattan method
• Calculate total no of squares moved horizontally
  and vertically to reach target ignoring diagonal
  movement and obstacles.

25
Can be faster to choose last one added to the
open list This biases the search in favor of
squares that get found later on in the search
when you have gotten closer to the target
26

• Each object in a game can have a number of states
  during its life.
• Model of behavior composed of
• States
• Transitions
• Actions
• Entry action
• Exit action
• Input action
• Transition action

27

• Advantage Can divide implementation of each
  games objects behavior into smaller fragments
• Easier to debug and extend

28
(No Transcript)
29

• Brain
• Receives input
• Processes input
• Communicates the output
• Working cooperation of the individual
  neurons within the network to operate

30

• ADWANTAGES
• Problems choosing appropriate input
• Neural networks sensitivity to changes in a
  games action logic and the need for re-training
  the network whenever such a situation occurs
• Rather complicated theory and difficulties with
  debugging in case of problems
• Time-consuming and complicated process of
  training the network

31
Conclusion

• Games With No AI
• Not possible!
• Every game with computer controlled
  characters/opponents uses some sore of AI
• Game AI has come a long way since the 1970s
• Future looks bright
• Neural networks are the future of computer games
  and a future that is not that distant anymore

32
(No Transcript)
33
(No Transcript)