Computational Intelligence in Games: An Overview

Computational Intelligence in Games: An Overview

Zahid HalimFaculty of Computer Science and Engineering

Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, [email protected]

Computational Intelligence in Games: An Overview 2

Layout

• What is AI/CI and ML• Why Computer Games?• How CI helps computer Games?• Some Examples• Key venues to publish work• Future directions

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AI vs. CI vs. ML

• Artificial Intelligence (Think like human, learn from experience, recognize patterns, make complex decisions based on knowledge and reasoning)

– Machine learning– Knowledge representation– Natural Language Processing– Planning Robotics etc.

• Machine learning– Branch of AI– Construction and study of systems that can learn from data– Email messages to learn to distinguish between spam and non-spam messages – There is difference between ML and Data Mining too

• Computational Intelligence (www.ieee-cis.org)– Integrating the fields

• Artificial Neural Networks• Evolutionary Computation• Fuzzy Logic

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They are related… But they are all different…

I hope all of us understand difference between hard and soft computing

AI

CI ML

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Why Computer Games?

49% of U.S. households own a dedicated game console

Male53%

Female47%

The average game player age is: 30 years

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32%

31%

37%

Under 18

18-32

36 or more


Why Computer Games?

• 42% of game players believe that computer and video games give them the most value for their money, compared with DVDs, music or going out to the movies

• Gamers who are playing more video games than they did three years ago are spending less time:

– 59% playing board games– 50% going to the movies– 47% watching TV– 47% watching movies at home

• 62% of gamers play games with others, either in-person or online• 78% of gamers who play with others do so at least one hour per week

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Money Matters!

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

66.9 7 7.3 6.9 7.3

9.5

11.7

1616.9 16.6

Dollars (Billions)

11%

23%

67%

Consumer Spend on Games Industry 2011

Accessories Hardware Contents

Total:

$24.75 Billion

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But its not every thing!


What can Computational Intelligence do?

• Generate complete game• Creation of intelligent game characters• Creation of entertaining game characters• Generating tracks for racing games. • Adaptable player experience. • Levels for action games. • Generating maps for games.

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Procedural Content Generation• Lindenmayer system: A variant of a formal grammar, most famously used to model the

growth processes of plant.• Consists of:

– An alphabet of symbols that can be used to make strings– A collection of production rules which expand each symbol into some larger string of

symbols– An initial "axiom" string from which to begin construction– A mechanism for translating the generated strings into geometric structures.

• PCG can also generate weapons that player might require in a game• Search based PCG is different

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Some of the PCG based Games

Game Content Year

ToeJam & Earl The random levels were procedurally generated. 1991

The Elder Scrolls III: MorrowindWater effects are generated on the fly."Water

Interaction" demo. 2002

RoboBlitz XBox360 live arcade and PC 2006

Borderlands Weapons were generated depending upon the levels 2009

Terraria2D landscape was generated that a player can travel

around. 2011

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Automated Game “entertaining” GenerationSearch Space

Dimension

Possible Values Select Values

CheckersChess

Play Area Only black squares are used

Both white & black squares are used

Both white & black squares are used

Types of Pieces Initially 1, maximum 2 6 6Number of pieces/type

12, variable (but max. 12)

16 variable but at maximum 24

Initial position Black squares of first 3 rows

Both white & black squares of first 2 rows

Both white & black squares of first 3 rows

Movement direction Diagonal forward and Diagonal, forward

backward

All directions, straight forward, straight

forward and backward, L shaped, diagonal

forward

All directions, straight forward, straight forward and backward, L

shaped, diagonal forward

Step Size One Step One Step, Multiple Steps

One Step, Multiple Steps

Capturing Logic Step over Step into Step over, step intoGame ending logic No moves possible for

a playerNo moves possible for

the kingNo moves possible for a player, no moves possible for the king

Conversion Logic Checkers into king Soldiers into queen or any piece of choice

Depends upon rules of the game

Mandatory to capture Yes No Depends upon rules of the gameTurn passing allowed No No No

Gene Title Value

1Placement of gene of each type 0-6:

24

25Movement logic of each type 1-6:

30

31-36 Step Size 0/1

37Capturing logic move into cell or jump over 0/1 0/1:

42

43 Piece of honour 0-6

44Conversion Logic 0-6 0-6:

49

50 Mandatory to capture or not 0/1

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Objective Function )/nL(=D k0K n)/nI(=I k0K

n

)/m))/n/)(C(((=Dynn

1j

m

1ii

iL

))/n||/))(C(((=Un

1i

m

0kk

Cu1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100

0

0.2

0.4

0.6

0.8

1

1.2

Duration of game (D)

Scale

d va

lue

of D

• 1+1 Evolutionary Strategy (ES)• 10 chromosomes are randomly initialized• The evolutionary algorithm is run for 100 iterations• Mutation only with probability of 30 percent • One parent produce one child

– Fitness difference is calculated– If it is greater than 4 (at least half times better) child is promoted to the next population

))/)((1(__ metricsallfor

pcp fitnessfitnessfitnessferenceFitnessDif

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Making Racing Fun Through Player Modelling and Track Evolution

• We have one or several car racing tracks with– Walls, Waypoints, Staring position of the car

• Car consist of– Sensor model to sense the environment– Discrete set of control commands

• Objective of the game is to pass as many waypoints in given timesteps.

• Car has 6 sensors, Speed of the car and Angle to the next waypoint

• Fully connected feedforward nets (MLPs) with the tanh transfer function. • Only the weights of the networks are changed by evolution or back propagation• Nine inputs (sensors and a bias input), Six hidden neurons• Two output neurons are used.

– The First output is interpreted as driving command – Second as steering command.

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Learning Behaviour: Backpropagation

• Human player drove a number of laps around a track, while the inputs from sensors and actions taken by the human were logged at each timestep.

• This log was then used to train a neural network controller to associate sensor inputs with actions using a standard backpropagation algorithm.

• Several variations on this idea were tried with very little success.• Training often achieved low error rates (typically 0.05), none of the trained networks

managed to complete even half a lap. • A small amount of noise that is applied to sensors guarantees that the car does not simply

replay the human action.

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Evolving Neural Network Agents in the NERO Video Game– real-time NeuroEvolution of Augmenting Topologies (rt-NEAT) method for evolving

increasingly complex artificial neural networks in real time, as a game is being played.

– rtNEAT makes possible a new genre of video games in which the player teaches a team of agents through a series of customized training exercises.

– In NEAT, the population is replaced at each generation.• Everyone’s behaviour would change at once. • Behaviours would remain static during the large gaps between generations

– In rtNEAT, a single individual is replaced very few game ticks

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Conferences and Journals

• IEEE Computational Intelligence and Games• IEEE Transactions on Computational Intelligence and AI in Games (IF 1.8)

• International Journal of Computer Games Technology• International Conference on Computer Games (CGAMES)• CGamesUSA International Conference on Computer Games

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Where are the opportunities?

• CIG for health care• CIG for education• Neuro Computer interface for games• Physicological study via games

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Thanks for your patience

Presentation available at: http://ming.org.pk/zahid.htm


Bibliography

• Halim, Zahid, A. Rauf Baig, and Hasan Mujtaba. "Measuring entertainment and automatic generation of entertaining games." International Journal of Information Technology, Communications and Convergence 1.1 (2010): 92-107.

• Halim, Zahid, A. Rauf Baig, and Mujtaba Hasan. "Evolutionary Search For Entertainment In Computer Games." Intelligent Automation & Soft Computing 18.1 (2012): 33-47.

• Halim, Zahid, and A. Raif Baig. "Evolutionary Algorithms towards Generating Entertaining Games." Next Generation Data Technologies for Collective Computational Intelligence. Springer Berlin Heidelberg, 2011. 383-413.

• http://tim.hibal.org/blog/wp-content/uploads/2010/01/speciesChange.png

• http://www.sennir.co.uk/Journal/178

• ESA 2012 Sales, Demographic and Usage Data

• Evolving Neural Network Agents in the NERO Video Game, Stanley et. al

• Acquiring Visibly Intelligent Behavior with Example-Guided Neuroevolution, Bryant et. al.

• Making Racing Fun Through Player Modeling, Togelius et. al.

• Evolutionary Search for Entertainment in Games, Halim et. al.

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Computational Intelligence in Games: An Overview

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