Measuring Entertainment and Automatic Generation of Entertaining Games

FAST-National University of Computer and Emerging Sciences, Islamabad

Measuring Entertainment and Automatic Generation of Entertaining Games

PhD Thesis Defense Zahid Halim

Date: 23rd November 2010http://ming.org.pk/zahid.htm

Supervised By: Dr. Rauf Baig

FAST-National University of Computer and Emerging Sciences, Islamabad 2

Presentation Outline

• Introduction– Problem Statement– Thesis Objective

• Contribution– Proposed Metrics– Board Based Games– Predator/prey Games

• Conclusion• Future Plans• Major Achievements• Questions• Bibliography


Problem Statement

• Abundance of Games

• Game Development Process

• Issues– Quantifying entertainment– Writing new games/versions


Thesis Objective

• Define entertainment in games

• Develop a quantitative measure of entertainment

• Computational Intelligence to generate entertaining games

• Verify evolved game’s entertainment


Entertainment Metrics Board Based Games

• Duration of the Game

• Intelligence for Playing the Game

• Dynamism Exhibited by the Pieces

• Usability of the Play Area


Duration of the Game Metrics

• Calculated by playing the game n times• Taking average number of moves over these n games • Maximum moves are fixed at 100

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 1010

0.2

0.4

0.6

0.8

1

1.2

Raw value of D

Duration of game (D)

Scal

ed v

alue

of D

)/nL(=D k0K n


Intelligence for Playing the Game Metrics

• Number of wins of an intelligent controller over one making random moves

• Higher number of wins against the random controller means that the game requires intelligence to be played and does not have too many frustrating dead ends

• IK is 1 if intelligent controller wins the game otherwise it is 0

)/nI(=I k0K n


Dynamism Exhibited by the Pieces Metrics

• Game whose rules encourage greater dynamism of movement in its pieces would be more entertaining

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

1j

m

1ii

iL


Usability of the Play Area Metrics

• It is interesting to have the play area maximally utilized during the game

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

1i

m

0kk

Cu


Combined fitness

• All chromosomes evaluated separately according to each of the four metrics

• Then the population is sorted on each of the metrics separately

• A rank based fitness is assigned to each chromosome.

• The best chromosome assigned the highest fitness

• Ranks multiplied by weights

) U (d )Dyn (c )I (b )D (a CF rrrr


Entertainment Metrics Predator/prey Games

• Duration of the Game

• Appropriate Level of Challenge

• Diversity

• Usability


Duration of the Game Metrics

• In order to evolve games of short to medium duration we have fixed the upper bound of steps to 100– 3 to 5 minute game if played with arrow keys

• Premature death of agent possible• The death possibility of the agent should not be very high

– Case the resulting games short and frustrating – Depend upon the agent playing the game

)/nL(=D k0K n


Appropriate Level of ChallengeMetrics

• High score, achieved easily and similarly too low – Not challenging enough

• Game rules should provide an appropriate level of challenge

• Factor of uncertainty in the rules of the game

)||(=C m

am

SSS

e


Diversity Metrics

• The diversity of the game is based upon the diversity of the pieces in the game

• The behavior of the moving pieces of the game should be sufficiently diverse so that it cannot be easily predicted

)/n))(d((=Divn

1i

m

0kk


Usability Metrics

• It is interesting to have the play area maximally utilized during the game

• If most of the moving pieces remain in a certain region of the play area then the resulting game may seem strange

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

1i

m

0kk

Cu


Combined fitness

• All chromosomes evaluated separately according to each of the four metrics

• Then the population is sorted on each of the metrics separately

• A rank based fitness is assigned to each chromosome

• The best chromosome assigned the highest fitness

• Ranks multiplied by weights

) U (d )Div (c )C (b )D (a CF rrrr


Board Based GamesSearch Space

Search Space DimensionPossible Values

Select ValuesCheckers Chess

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 rowsBoth white & black squares of first 3

rowsMovement direction Diagonal forward and

Diagonal, forward backwardAll 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 StepsCapturing 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 kingConversion Logic Checkers into king Soldiers into queen or any

piece of choiceDepends upon rules of the game

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


Chromosome Encoding

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


Random Controller

1. Input: Game Board current state

2. Generate all legal moves3. Store the moves in a queue4. Shuffle the queue5. If not mandatory to kill

6. Randomly select a move from the queue.

7. Else

8. Select a move that captures an opponent's piece, if such move exists

9. Otherwise, randomly select a move from the queue.

10. Output: Next move to take


Min-Max based Controller

1. Input: Game Board current state1. For each piece

2. priority=0

3. For each piece

4. if is piece of honor

5. priority = priority +1 000

6. if movement logic all directions

7. priority = priority + 8

8. if movement logic diagonal Forward and Backward


10. if movement logic Straight Forward and Backward


12. if movement logic diagonal Forward


14. if movement logic Straight Forward


16. if movement logic L shaped


18. if capturing logic step into


20. if capturing logic step over


22. Count the number of pieces of Player A

23. Multiply the number of pieces of a type with its relevant priority

24. Count the number of pieces of Player B

25. Multiply the number of pieces of a type with its relevant priority

26. Calculate boardValue = WeightSumofA-WeightSumofB

27. Check if the Piece of Honour is dead add -1000 to boardValue

28. Check if the Piece of Honour is NOT dead add +1000 to boardValue

29. Output: boardValue


Experimentation Setup

• 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(__

metricsallforpcp fitnessfitnessfitnessferenceFitnessDif


1 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 97100

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Duration Intelligence Dynamism Usability

Metrics values of one family


Piece No Movement Logic Step Size

Capturing Logic

Conversion Logic

1 L Multiple Step Into 6

2Diagonal Forward &

Backward Single Step Over 53 All Directions Multiple Step Into Nil4 Straight Forward Multiple Step Into 15 Straight Forward Multiple Step Over 26 All Directions Multiple Step Over 3

Piece of Honour 5

Mandatory to Capture No

Game Rules/Pieces Positions


Learnability of Evolved Games 1/2

• Schmidhuber’s theory of artificial curiosity • Chellapilla’s architecture of the controller for checkers player• 5 layers in the ANN

– Input with 64 neurons– First hidden layer with 91 neurons– Second hidden layer with 40 neurons – Third with 10 neurons – Output layer with 1 neuron. – Hyperbolic tangent function is used in each neuron– Connection weights range is [-2, 2]


Learnability of Evolved Games 2/2

• The training of the ANN is done using co-evolution• GA population is initialized representing weight• Each individual played against randomly selected 5 others• Mutation only

Game 1 Game 2 Game 3 Game 40

20

40

60

80

100

120

LearnabilityIt

erati

on to

ach

ieve

max

imum

fitn

ess


User Survey

• Human user survey on 10 subjects• Chosen such that they have at least some level of interest towards

computer games

1 2 3 40

102030405060708090

100

Game Liked (%)


Predator/prey GamesSearch Space

• 14 X 14 grid excluding the boundary walls.

• Couple of walls at fixed positions and of size 7 cells

• There is one player controlled by the human player.

• There are N (0-20)other pieces of M (1,2 and 3) types

• Maximum duration 100 game steps• Finish game

– Agent dies– Maximum score is achieved – Maximum game steps utilized

• Movement logic

– No movement – Clockwise– Counter clockwise– Random– Random direction

• Collision logic– no effect– random relocation to a new location

on the grid – death

• Scoring logic– +1, -1, 0


Chromosome Encoding

Number of predators Red 0-20

Collision logic

Blue-Green 0-2

Green 0-20 Blue-Blue 0-2

Blue 0-20 Blue-Agent 0-2

Movement logicRed 0-4 Agent-Red 0-2

Green 0-4 Agent-Green 0-2

Blue 0-4 Agent-Blue 0-2

Collision logic

Red- Red 0-2

Score logic

Red- Red -1,0,+1

Red- Green 0-2 Green-Green -1,0,+1

Red-Blue 0-2 Blue-Blue -1,0,+1

Red- Agent 0-2 Agent-Red -1,0,+1

Green-Red 0-2 Agent Green -1,0,+1

Green-Green 0-2 Agent-Blue -1,0,+1

Green-Blue 0-2 Green-Red -1,0,+1

Green-Agent 0-2 Blue-Red -1,0,+1

Blue-Red 0-2 Blue-Green -1,0,+1


Rule Based Controller

• The controller notes the nearest piece (if any) in each of the four directions moves one step towards the nearest score increasing piece

• If there are no score increasing piece, step according to priority list

– Move in the empty direction• If more than one such directions move towards farthest

– Move towards score neutral piece

– Move towards score decreasing piece

– Move towards death causing piece


Neural Network Based Controller

• Multi-layer fully feed forward • 6 neurons in the input layer• 5 neurons in the hidden layer • 4 output layer neurons• Sigmoid activation function• Edges weights -5 to +5

∆xr

∆xg

∆xb

∆yg

∆yb

∆yr Nu

Nd

Nl

Nr

Connection Edges

Connection Edges

Connection Edges


Experimentation Setup

• 10 chromosomes are randomly initialized by the GA

• One offspring is created for each chromosome – Duplicating it– Mutating any one of its gene

• Results in 20 chromosomes from which 10 best chosen

• 100 generations


Appropriate level of challenge

Duration of game

Diversity

Usability

Combined Fitness


Controller Learning Ability

Diversity-RB

Duration-RB

Challenge-RB

Usability-RB

Diversity-ANN

Duration-ANN

Challenge-ANN

Usability-ANN

Combined-RB

Combined-ANN

Random

0 200 400 600 800 1000 1200


User Survey

• 10 subjects• Conducted in two different sets on different days

– Rule based controller – ANN based controller– Each individual was given 6 games– Play 2 times

Random

Duration

Challenge

Diversi

ty

Usabilit

y

Combined Fi

tness02468

1012

Rule Based ControllerANN Based Controller

Duration4%

Chal-lenge32%

Usability24%

Combined Fitness

40%

Human User Survey ANN Based Controller

Duration12%

Challenge24%

Usability18%

Com-bined Fitness

47%

Human User Survey Rule Based Controller


Conclusion

• Identified the entertainment factors• Introduced entertainment metrics

– Board based genre of games– Video games

• Predator/prey • Entertainment factors dependent on genre• Automatic generation of entertaining games• Verification

– Learnability of Evolved Games– User survey


Limitations

• Appropriate for offline mode

• Processor intensive– Multiple times


Future Plans

• Multi objective genetic algorithm

• Model the behavior of a particular human player– Evolving content for games against his/her playing patterns

• Physical activating games for medical science

FAST-National University of Computer and Emerging Sciences, Islamabad

Measuring Entertainment and Automatic Generation of Entertaining Games

Thank you for your patience

Questions


Bibliography 1/7

• J.Schmidhuber, ”Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts”, Connection Science, vol. 18, pp.173–187, 2006

• N. Esposito, “A Short and Simple Definition of What a Videogame Is”, in proceedings of Digital Games Research Association (DiGRA), Vancouver, Canada, 16-20 June, 2005

• J.Smed and H.Hakonen, "Towards a Definition of a Computer Game", Technical Report, Computer Games Research Group, Department of Information Technology, University of Turku, Finland, 2005

• S. Lucas,” Evolving a neural network location evaluator to play Ms. Pac-Man. In Graham Kendall and Simon Lucas, editors”, Proceedings of the IEEE Symposium on Computational Intelligence and Games, pages 203–210, Essex University, Colchester, UK, 4–6 April 2005

• E. Norlig and L. Sonenberg, ” An approach to evaluating human characteristics in agents”, In Gabriela Lindemann, Daniel Moldt, Mario Paolucci, and Bin Yu, editors, in proceedings of the International Workshop on Regulated Agent-Based Systems: Theories and Applications , Bologna, Italy, 16 th July 2002

• K. O. Stanley, N. Kohl, R. Sherony, and R. Miikkulainen,” Neuroevolution of an automobile crash warning system”, in proceedings of the Genetic and Evolutionary Computation Conference , Washington DC, USA, 25-29 June, 2005

• D. A. Jirenhed, G. Hesslow, and T.Ziemke, “Exploring internal simulation of perception in mobile robots”, in proceedings of the Fourth European Workshop on Advanced Mobile Robots, Lund, Sweden, 19-21 September, 2001

• C. Schlieder, P. Kiefer, S. Matyas,” Geogames: Designing Location-Based Games from Classic Board Games”, IEEE Intelligent Systems 21(5): 40-46, 2006

• H. Lund, T. Klitbo and C. Jessen, "Playware Technology for Physically Activating Play", Artificial Life and Robotics Journal, 165-174, 2005

• L. Davis, “Hybridization and Numerical Representation”, in L Davis (ed), The Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991, pp 61–71


Bibliography 2/7

• I.Witten, and E.Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, 2nd Edition, Morgan Kaufman, San Francisco, CA, 2004

• I. Rechenberg, “Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der Biologischen Evolution”, Frammann-Holzboog Verlag, Stuttgart, 1973

• K. Chellapilla, D. B. Fogel, ”Evolving an expert checkers playing program without using human expertise”, IEEE Trans. Evolutionary Computation 5(4): 422-428, 2001

• P. J. Angeline, G. M. Sauders, and J. B. Pollack, “ An evolutionary algorithm that constructs recurrent neural networks”, IEEE Transaction on Neural Networks, 5:54–65, 1994

• T. Back and H. P. Schwefel, “An overview of evolutionary algorithms for paramater optimization”, Evolutionary Computation, 1(1):1–23, 1993

• H. Iida, N. Takeshita, and J. Yoshimura, “A Metric for Entertainment of Board Games: Its Application for Evolution of Chess Variants”, In Nakatsu, R., and Hoshino, J., eds., Entertainment Computing: Technologies and Applications (Proceedings of IWEC 2002), pages 65-72. Boston, MA: Kluwer Academic Publishers, 2003

• A. Cincotti and H. Iida, “Outcome Uncertainty And Interestedness In Game-Playing: A Case Study Using Synchronized Hex”, In New Mathematics and Natural Computation (NMNC), vol. 02, issue 02, pages 173-181, 2006

• S. Retalis, “Creating Adaptive e-Learning Board Games for School Settings Using the ELG Environment”, Journal of Universal Computer Science: 2897-2908, 2008

• J. Togelius, R. D. Nardi, and S. M. Lucas,“Towards automatic personalised content creation for racing games”, in Proceedings of the IEEE Symposium on Computational Intelligence and Games, Piscataway, NJ, 1-5 April, 2007

• S. Sevenster, A.P. Engelbrecht, “GARTNet: A Genetic Algorithm for Routing in Telecommunications Networks”, Proceedings of CESA96 IMACS Multiconference on Computational Engineering in Systems Applications, Symposium on Control, Optimization and Supervision, Lille, Prance, Vol 2, 1996


Bibliography 3/7

• G. N. Yannakakis , J. Hallam, ”Towards Optimizing Entertainment In Computer Games”, Applied Artificial Intelligence”, v.21 n.10, p.933-971, November 2007

• M. Gallagher and A. Ryan, “Learning to Play Pac-Man: An Evolutionary Rule-based Approach,” in proceedings of IEEE Congress on Evolutionary Computation, Canberra, Australia, 8-12 December. 2003

• G. Lankveld, P. Spronck, M. Rauterberg, “Difficulty Scaling through Incongruity”, in proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, Stanford, California, 22–24 October, 2008

• J. Togelius and J. Schmidhuber, "An Experiment in Automatic Game Design", in Proceedings of IEEE Computational Intelligence and Games, Perth, Australia, 15 - 18 December, 2008

• J. Togelius, S. Karakovskiy, J. Koutnik and J. Schmidhuber, "Super Mario Evolution", in proceedings of IEEE Symposium on Computational Intelligence and Games, Milano, Italy, 7-10 September, 2009

• N. Lazzaro, “Why We Play Games: Four Keys to More Emotion Without Story”, in proceedings of Game Developers Conference, San Jose, California, 8th March, 2004

• C. Pedersen, J. Togelius, and G. N. Yannakakis, "Optimization of platform game levels for player experience,'' in proceedings of Artificial Intelligence and Interactive Digital Entertainment , Stanford, California, 14-16 October 2009

• K.Compton and M. Mateas, “Procedural Level Design for Platform Games”, in proceedings of 2nd Artificial Intelligence and Interactive Digital Entertainment Conference, Stanford, 20-23 June, California, 2006

• G. N. Yannakakis and J. Hallam, “Evolving Opponents for Interesting Interactive Computer Games”, in proceedings of the 8th International Conference on the Simulation of Adaptive Behavior, Los Angeles, USA, 13-17 July 2004

• B. D. Bryant and R. Miikkulainen, “Acquiring visibly intelligent behavior with example-guided neuroevolution”, in proceedings of the 22nd national conference on Artificial intelligence, Vancouver, British Columbia, Canada, 22-26 July, 2007


Bibliography 4/7

• G. N. Yannakakis, and J. Hallam, "Entertainment Modeling through Physiology in Physical Play," International Journal of Human-Computer Studies, vol. 66, issue 10, pp. 741-755, October, 2008

• R. J. Orr and G. D. Abowd, “The smart floor: a mechanism for natural user identification and tracking”, in proceedings of Conference on Human Factors in Computing Systems, Netherlands, 1-6 April, 2000

• B. Richardson, K. Leydon, M. Fernstrom, and J. A. Paradiso, “Z-tiles: Building blocks for modular, pressure-sensing floorspaces”, in proceedings of Conference on Human Factors in Computing Systems, Vienna, Austria, 24-29 April, 2004

• H. H. Lund, T. Klitbo, and C. Jessen, “Playware technology for physically activating play”, Artifical Life and Robotics Journal, vol. 9, no. 4, pp. 165–174, 2005

• K.Grønbæk, O.S.Iversen, K.J.Kortbek, K.R.Nielsen, and L. Aagaard, " iGameFloor - a Platform for Co-Located Collaborative Games", in proceedings of the International Conference on Advances in Computer Entertainment, Salzburg, Austria, 13.-15. June, 2007

• N. Beume, H. Danielsiek, C. Eichhorn, B. Naujoks, M. Preuss, K. Stiller, S. Wessing, “Measuring Flow as Concept for Detecting Game Fun in the Pac-Man Game”, in proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China, 1-6 June, 2008

• K.O.Stanley, B.D.Bryant, R.Miikkulainen, “Evolving neural network agents in the nero video game”, in proceedings of IEEE Symposium on Computational Intelligence and Games, Milan, September, 2009

• B. D. Bryant and R. Miikkulainen, "Evolving stochastic controller networks for intelligent game agents," in proceedings of IEEE Congress on Evolutionary Computation, San Francisco, California, 26-29 June, 2006

• B. D. Bryant and R. Miikkulainen, “Evolving visibly intelligent behavior for embedded game agents”, PhD thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, 2006

• D.L. Roberts, C.R. Strong and C.LIsbell,” Estimating player satisfaction through the author's eyes”, in proceedings of the Second Workshop on Optimizing Player Satisfaction, California, USA,6-8 July, 2007


Bibliography 5/7

• B. D. Bryant and R. Miikkulainen, ”Exploiting sensor symmetries in example-based training for intelligent agents”, In Louis, S. J., and Kendall, G., eds., Proceeedings of the IEEE Symposium on Computational Intelligence and Games, Reno Tahoe, 22-24 May, 2006

• B. D. Bryant and R. Miikkulainen, “Neuroevolution for adaptive teams,” in proceedings of IEEE International Conference on Electronic Commerce, Newport Beach, CA, USA, 24-27 June 2003

• N. Beume, T. Hein, B. Naujoks, N. Piatkowski, M. Preuss, M. and S. Wessing, “Intelligent Anti-Grouping in Real-Time Strategy Games”, in proceedings of IEEE Symposium on Computational Intelligence and Games Perth, Australia, 15 - 18 December, 2008

• M. Csíkszentmihályi, “Flow: The Psychology of Optimal Experience”, Harper and Row. ISBN 0-06-092043-2, 1991.• A. Bezek, “Modeling Multiagent Games Using Action Graphs”, in proceedings of Modeling Other Agents from Observations, New York, USA, 19 th July,

2004

• B. Christian Bauckhage, C. Gorman, Thurau & M. Humphrys, "Learning Human Behavior from Analyzing Activities in Virtual Environments", MMI interaktiv journal, Issue: 12 Pages: 3-17, 2007

• T. Henderson and S. Bhatti, “Modelling user behaviour in networked games”, Multimedia 2001, June 2001

• H. Barber and D. Kudenko, “A User Model for the Generation of Dilemma-based Interactive Narratives”, in proceeding of third Artificial Intelligence for Interactive Digital Entertainment Conference, California, USA, 6-8 June, 2007

• R. Hunicke and V. Chapman, “ AI for Dynamic Difficulty Adjustment in Games”, in proceedings of the Challenges in Game AI Workshop, Nineteenth National Conference on Artificial Intelligence, San Jose, California, USA, 25-29 July, 2004

• N.A.Taatgen, and J.R.Anderson, “Why do children learn to say “Broke”? A model of learning the past tense without feedback.”, Cognition, 86(2),123-155, December 2002


Bibliography 6/7

• D. Thue, V. Bulitko, M. Spetch, E. Wasylishen,” Interactive Storytelling: A Player Modelling Approach”, in proceeding of third Artificial Intelligence for Interactive Digital Entertainment Conference, California, USA, 6-8 June, 2007

• M. Kriegel, R. Aylett, J. Dias and A. Paiv, ”Learning Player Preferences to Inform Delayed Authoring”, in AAAI Symposium on. Intelligent Narrative Technologies. Westin Arlington Gateway, Arlington, Virginia, 9-11 November, 2007

• P. Rani, N. Sarkar and C. Liu, “Maintaining Optimal Challenge in Computer Games Through Real-Time Physiological Feedback”, 11th Human Computer Interaction International, Vegas, Nevada, USA, 22-27 July 2005

• S. W. McQuiggan, S. Lee and J. C. Lester, “Predicting User Physiological Response for Interactive Environments: An Inductive Approach”, in proceeding of third Artificial Intelligence for Interactive Digital Entertainment Conference, California, USA, 20-23 June, 2007

• R. Picard, E. Vyzas and J. Healey, ”Toward machine emotional intelligence: analysis of affective physiological state”, in IEEE Transactions Pattern Analysis and Machine Intelligence 23(10): 1185-1191,2001

• K. P. Pomsta and H. Pain, “Providing Cognitive and Affective Scaffolding through Teaching Strategies”, in proceedings of 7th International Conference on Intelligent Tutoring Systems, Maceió, Brazil, 31st August, 2004

• H.Prendinger, S.Mayer, J.Mori, and M.Ishizuka, “Persona effect revisited: Using bio-signals to measure and reflect the impact of character-based interfaces”, in proceedings of 4th International Conference on Intelligent Virtual Agents, Kloster Irsee, Germany, 15-17 September , 2003

• N. A. Taatgen, M. V. Oploo, J. Braaksma, J. Niemantsverdriet,” How to Construct a Believable Opponent using Cognitive Modeling in the Game of Set”, in proceedings of the fifth international conference on cognitive modelling, Bamberg, Germany. 9-12 April, 2003

• N. Beume, B. Naujoks, G. Neugebauer, N. Piatkowski and M. Preuss , “CI enlivened ghosts for Pac-Man”, in proceedings of IEEE Computational Intelligence And Games, Perth, Australia, 15-18 September, 2008


Bibliography 7/7

• G. V. Lankveld, P. Spronck, and M. Rauterberg, ”Incongruity-Based Adaptive Game Balancing”, Advances In Computer Games, Pamplona, Spain, 11 - 13 May 13, 2009

• N. Beume, T. Hein, B. Naujoks, N. Piatkowski, M. Preuss, S. Wessing, “Intelligent Anti-Grouping in Real-Time Strategy Games”, In Proceedings of IEEE Computational Intelligence and Games, Perth, Australia, 15 - 18 December, 2008

• H. Danielsiek, R. Stüer, A. Thom, N. Beume, B. Naujoks and M. Preuss, “Intelligent Moving of Groups in Real-Time Strategy Games”, in proceedings of IEEE Computational Intelligence And Games, Perth, Australia, 15-18 September, 2008

• M. Csikszentmihalyi and I. Csikszentmihalyi, “Introduction to Part IV in Optimal Experience”, Psychological Studies of Flow in Consciousness, Cambridge, UK: Cambridge University Press. 1988

• T. W. Malone, “ What makes computer games fun?”, ACM SIGSOC Bulletin, Volume 13 , Issue 2-3 1982

• Presentation Chess Pieces Image, https://swansea-edunet.gov.uk/en/schools/Pontarddulais/PageImages/Chess%20Piece.jpg, 05-11-2010

• Presentation background Images, http://www.presentermedia.com, 05-11-2010

• R. Koster, “ A Theory of Fun for Game Design”, Paraglyph Press, 2005

• D. H. Ackley and M. L. Littman,”Interactions between learning and evolution”, In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, pages 478–507, Reading, Sante Fe Institute Studies in the Sciences and Complexity, Addison-Wesley, MA, 1992

Date post:	23-Feb-2016
Category:	Documents
Upload:	yahto
View:	45 times
Download:	0 times

Measuring Entertainment and Automatic Generation of Entertaining Games

Documents