Biologically inspired artificial intelligence for computer games

Biologically Inspired Artificial Intelligence for Computer Games

Volumn , Issue , 2007, Pages 1-262

  Charles, Darryl ^a   Fyfe, Colin ^b   Livingstone, Daniel ^b   McGlinchey, Stephen ^b  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS;

ACADEMIC RESEARCH; ARTIFICIAL IMMUNE SYSTEM; ARTIFICIAL INTELLIGENCE TECHNIQUES; BIOLOGICALLY INSPIRED; HUMAN PLAYERS; INTELLIGENCE SOFTWARE; PROGRAMMING COMMUNITY; TECHNOLOGICAL RESEARCHES;

COMPUTER GAMES;

EID: 70349278428     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-646-4     Document Type: Book

References (171)

1. Brooks, R.A. (1991). Intelligence without representation. Artificial Intelligence Journal, 47, 139-159.
2. Champandard, A. (2003). AI game development. New Riders.
3. Fairclough, C., & Cunningham, P. (2004, November). AI structuralist storytelling in computer games. In Proceedings of the CGAIDE International Conference on Computer Games: Artificial Intelligence, Design and Education, Reading (pp. 8-10).
4. Ginsberg, M. (1993). Essentials of artificial intelligence. Morgan Kaufmann.
5. Grand, S., & Cliff, D. (1998). Creatures: Artificial life autonomous software agents for home entertainment. Autonomous Agents and Multi-Agent Systems, 1(1), 39-57.
6. Holland, J.H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
7. Hunter, W. (1998). The dot eaters: Classic video game history. Retrieved October 12, 2006, from http://www.emuunlim.com/doteaters/
8. Kent, S. (2001). The ultimate history of videogames. Prima Life.
9. Langton, C.G. (1989). Artificial life. SFI studies in the sciences of complexity. Addison-Wesley.
10. th International Conference on Intelligent Games and Simulation, London.
11. Poole, S. (2000). Trigger happy: Videogames and the entertainment revolution. Arcade Publishing.
12. Rabin, S. (2002a). Implementing a state machine language. In S. Rabin (Ed.), AI game programming wisdom (pp. 314-320). Hingham, MA: Charles River Media, Inc.
13. Rabin, S. (2002b). Enhancing a state machine language through messaging. In S. Rabin (Ed.), AI game programming wisdom (pp. 321-329). Hingham, MA: Charles River Media, Inc.
14. Rabin, S. (Ed.). (2002c). AI game programming wisdom. Hingham, MA: Charles River Media.
15. Rabin, S. (Ed.). (2004). AI game programming wisdom II. Hingham, MA: Charles River Media.
16. Reynolds, C.W. (1987). Flocks, herds and schools: A distributed behavioural model. Computer Graphics, 21(4).
17. Reynolds, C.W. (1999). Steering behaviours for autonomous characters. Paper presented at the Game Developers Conference, San Jose, California. San Francisco: Miller Freeman Game Group.
18. th Conference on Computer Science.(pp. 124-130).
19. Sethian, J.A. (1998). Level set methods and fast marching methods: Evolving interfaces in computational geometry, fluid mechanics, computer vision and materials sciences. Cambridge University Press.
20. Stout, B. (1996, October). Smart moves: Intelligent pathfinding. Game Developer.
21. Young, R.M., Riedl, M.O., Branly, M., Jhala, A., Martin, R.J., & Saretto, C.J. (2004). An architecture for integrating plan-based behaviour generation with interactive game environments. Journal of Game Development, 1(1).
22. Barto, A.G., Sutton, R.S., & Anderson, C.W. (1983). Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, 13, 834-846.
23. Haykin, S. (1994). Neural networks: A comprehensive foundation. MacMillan.
24. McCulloch, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133.
25. Minsky, M.L., & Papert, S.A. (1969). Perceptrons. Cambridge, MA: MIT Press.
26. Willshaw, D.J., & von der Malsburg, C. (1976). How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London Series B, 194, 431-445.
27. Ackley, D.H., Hinton, G.E., & Sejnowski, T.J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147-169.
28. Haykin, S. (1994). Neural networks: A comprehensive foundation. MacMillan
29. Rummelhart, D. E., & McClelland, J.L. (Eds). (1986). Parallel distributed processing (Vols. 1-2). MIT Press.
30. Zell, A., et al. (2006). Stuttgart neural network simulator. Retrieved December 5, 2006, from http://www-ra.informatik.uni-tuebingen.de/SNNS/
31. Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavioural sciences. Unpublished doctoral thesis, Harvard University.
32. Widrow, B. J., & Hoff, M.E. (1960). Adaptive switching circuits. Paper presented at the IRE WESCON Convention Record (pp. 96-104).
33. st.World Conference on Digital. Games, University of Utrecht, The Netherlands.
34. Penton, R. (2003). Data structures for game programmers. Premier Press.
35. Nelson, M. (2001). Robocode. Retrieved February 5, 2007, from http://www.alphaworks.ibm.com/tech/robocode
36. Stewart, D. (2004, May). Java robocode AI investigation (honours computing project). University of Ulster.
37. Alexander, T. (2002). GoCap: Game observation capture. In S. Rabin (Ed.), AI game programming wisdom (pp. 579-585). Hingham, MA: Charles River Media.
38. Bishop, C., Svensen, M., & Williams, C. (1997). GTM: The generative topographic mapping. Neural Computation 10(1), 215-234.
39. Grossberg, S. (1991). Adaptive pattern classification and universal recoding, II: Feeback, expectation, olfaction and illusions. In Carpenter & Grossberg (Eds.), Pattern recognition in self-organising neural networks. Cambridge, MA: MIT Press.
40. Hebb, D. O. (1949). The organization of behaviour. Wiley.
41. Hertz, J., Krogh, A., & Palmer, R. G. (1991) Introduction to the theory of neural computation. Addison Wesley.
42. Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. Wiley
43. Kohonen, T. (1996). The speedy SOM (Tech. Rep. No. A33). Espoo, Finland: Helsinki University of Technology, Laboratory of Computer and Information Science.
44. nd ed.). New York: Springer-Verlag.
45. Luttrell, S.P. (1997). Self-organisation of multiple winner-take-all neural networks. Connection Science 9(1), 11-30
46. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Fifth Berkeley Symposium on Mathematics, Statistics and Probability (Vol. 1, pp. 281-297). Berkeley: University of California Press
47. Oja, E. (1982). A simplified neuron model as a principal component analyzer. Journal of mathematical biology, 15, 267-273.
48. Oja, E. (1989). Neural networks, principal components and subspaces. International Journal of Neural Systems, 1, 61-68.
49. Oja, E., Ogawa, H., & Wangviwattana, J. (1992). Principal component analysis by homogeneous neural networks (Parts I and II). IEICE trans on Information and system., (75), 366-375.
50. Ritter, H., Martinez, T., & Schulten, K. (1992). Neural computation and self-organizing maps. Reading, MA: Addison-Wesley
51. Sanger, T. D. (1989). An optimality principle for unsupervised learning. Advances in Neural Information Processing Systems, 1, 11-19.
52. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379-423.
53. Spronck, P., Sprinkhuizen, I., & Postma, E. (2002) Improved opponent intelligence through offline learning. International Journal of Intelligent Games & Simulation, 2(1), 20-27.
54. van Waveren, J. M. P., & Rothkrantz, L. J. M. (2002). Artificial player for quake III arena. International Journal of Intelligent Games.&.Simulation, 1(1), 25-32.
55. Zubek, R., & Khoo, A. (2002). Making the human care: On building engaging bots. In Proceedings of AAAI Spring Symposium on Artificial Intelligence and Interactive Entertainment, Menlo Park, California (AAAI Tech. Rep. SS-02-01). AAAI Press.
56. nd ed.). Prentice Hall.
57. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence Journal, 47, 139-159.
58. Buckland, M. (2002). AI techniques for game programming. Ohio: Premier Press.
59. Chaperot, B., & Fyfe, C. (2005a, November). Issues in the physics of a motocross simulation. In Q. Mehdi & N. Gough (Eds.), Game-On 2005. Leicester.
60. Chaperot, B., & Fyfe, C. (2005b, November 8-9). Motocross and artificial neural networks. In M. Merabti, N. Lee, M. H. Overmars, & A. El Rhalibi (Eds.), Game design and technology workshop. Liverpool.
61. Davis, L. (1991). Handbook of genetic algorithms. Van Nostrand Reinhold.
62. Grand, S., & Cliff, D. (1998). Creatures: Artificial life autonomous software agents for home entertainment. Autonomous Agents and Multi-Agent Systems, 1(1), 39-57.
63. Harvey, I., Husbands, P., Cliff, D., Thompson, A., & Jakobi, N. (1997). Evolutionary robotics: The Sussex approach. Robotics and Autonomous Systems, 20(2), 205-224
64. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michegan Press.
65. Kitano, H. (1990). Designing neural networks using genetic algorithms with graph generation system. Complex Systems, 4, 461-476.
66. Miller, G. F., Todd, P. M., & Hegde, S. U. (1989). Designing neural networks using genetic algorithms. In D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kauffman.
67. Sims, K. (1994). Evolving 3D morphology and behavior by competitio. In R. Brooks & P. Maes (Eds), Proceedings of artificial life IV (pp. 28-39). MIT Press.
68. Stanley, K. O., Bryant, B. D., & Miikkulainen, R. (2005, April). Evolving neural network agents in the NERO video game. In G. Kendall & S. Lucas (Eds.), IEEE 2005 Symposium on Computational Intelligence and Games.(CIG'05) (pp. 182-189). Essex: Colchester.
69. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2).
70. Axelroord, R. (1990). The evolution of cooperation. Penguin Books.
71. Baluja, S. (1994). Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning (Tech. Rep. CMU-CS-94-163). Carnegie Mellon University.
72. Ben-Jacob, E. (1998a). Bacterial wisdom. Physica A, 249, 553-557.
73. Ben-Jacob, E. (1998b). Bacterial wisdom, Godel's theorem and creative genomic webs. Physica A, 248, 57-76.
74. Champandard, A. J. (2004). AI game development. New Riders Publishing.
75. Colman, A. M. (1982). Game theory and experimental games. Oxford, England: Pergamon Press.
76. Dasgupta, D. (1993). Optimisation in time-varying environments using structured genetic algorithms (Tech. Rep. IKBS-17-93). U.K.: The University of Strathclyde.
77. Davis, L. (1991). Handbook of genetic algorithms. Van Nostrand Reinhold.
78. Fyfe, C. (1999). Structured population-based incremental learning. Soft Computing, 2(4), 191-198.
79. Holland, J. (1975). Adaptation in natural artificial systems. Ann Arbor: University of Michigan Press.
80. Rapoport, A. (2001). N-Person game theory, concepts and applications. University of Michigan Press.
81. Wang, T.-D. (2002). The evolution of cooperation in artificial communities. Unpublished doctoral thesis, University of Paisley.
82. Adami, C., Belew, R. K., Kitano, H., & Taylor, C. E. (1998). Artificial life VI. UCLA: MIT Press.
83. Bingham, C. (1998). Robert the Bruce. London: Constable and Company.
84. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139-159.
85. Buro, M. (2004, July). Call for AI research in RTS games. In D. Fu & J. Orkin (Eds.), AAAI workshop on challenges in game AI (pp. 139-141). San Jose.
86. Cliff, D., & Miller, G. F. (1995). Tracking the red queen: Measurements of adaptive progress in co-evolutionary simulations. Paper presented at the Third European conference on Artificial Life (pp. 200-218). Springer-Verlag.
87. Corruble, V., Madeira, C., & Ramalho, G. (2002). Steps towards building a good AI for complex wargame-type simulation games. Game-On 2002. In Q. Mehdi, N. Gough, & M. Cavazza (Eds.), The third international conference on intelligent games and simulation (pp. 155-159). London.
88. Dasgupta, D. (2000). Artificial immune systems and their applications. Springer-Verlag.
89. de Jong, E. D. (2004a, September). Hierarchical genetic algorithms. Paper presented at the 8th International Conference on Parallel Problem Solving from Nature (pp. 232-241), Birmingham, U.K.
90. de Jong, E. D. (2004b, September). Intransitivity in coevolution. Paper presented at the 8th International Conference on Parallel Problem Solving from Nature (pp. 843-851), Birmingham, U.K.
91. Kent, T. (2004). Multi-tiered AI layers and terrain analysis for RTS games. In S. Rabin (Ed.), AI game programming wisdom 2. Hingham, MA: Charles River Media, Inc.
92. Madeira, C., Corruble, V., Ramalho, G. & Ratitch, B. (2004, July). Bootstrapping the learning process for the semi-automated design of a challenging game AI. In D. Fu & J. Orkin (Eds.), AAAI workshop on challenges in game AI (pp. 72-76). San Jose.
93. Makepeace, D., & Padfield, A. (2004). Trench hell and the role of the 'Stosstruppen.' Retreived October 5, 2006, from http://www.wargamesjournal.com/
94. Ramsey, M. (2004). Designing a multi-tiered AI framework. In S. Rabin (Ed.), AI game programming wisdom 2. Hingham, MA: Charles River Media, Inc.
95. Rebollo, M., Botti, V., & Onaindia, E. (2003, June). Formal modeling of dynamic environments for real-time agents. In V. Marík, J. Müller, & M. Pchouek (Eds.), CEEMAS 2003 (Lecture Notes in AI, 2691) (pp. 474-484). Prague: Springer
96. Reynolds, J. (2002). Tactical team AI using a command hierarchy. In Rabin S. (Ed.), AI game programming wisdom. Hingham, MA: Charles River Media, Inc.
97. Rollings, A., & Morris, D. (1999). Game architecture and design. Scottsdale, AZ: Coriolis.
98. Steuer, E. (1986) Multiple criteria optimization: Theory, computation, and application. New York: Wiley.
99. Sweetser, P., Watson, M., & Wiles, J. (2003). Intelligent command agents for command and control simulations using cognitive work Analysis and machine learning. The University of Queensland, Brisbane, Australia. Retrieved June 14, 2007, from http://www.itee.uq.edu.au/~penny/
100. van der Sterren, W. (2002a). Squad tactics: Planned maneuvers. In S. Rabin (Ed,), AI game programming wisdom 2. Hingham, MA: Charles River Media, Inc.
101. van der Sterren, W. (2002b). Squad tactics: Team AI and emergent maneuvers. In S. Rabin (Ed.), AI game programming wisdom 2. Hingham, MA: Charles River Media, Inc.
102. Wallace, N. (2004). Hierarchical planning in dynamic worlds. In S. Rabin (Ed.), AI game programming wisdom 2. Hingham, MA: Charles River Media, Inc.
103. Watson, R. A., & Pollack, J. B. (2001). Coevolutionary dynamics in a minimal substrate. L. Spector & et al. (Eds.), Genetic and evolutionary computation conference (GECCO 2001). Morgan Kaufmann.
104. Whiteson, S., & Stone, P. (2003). Concurrent layered learning. Paper presented at the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems (pp. 193-200), Melbourne. ACM Press.
105. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on. Evolutionary Computation, 3(4), 257-271.
106. Binmore, K. (1992). Fun and games. DC: Heath and Co.
107. Dasgupta, D. (2000). Artificial immune systems and their applications. Springer-Verlag.
108. De Castro, L. N., & von Zuben, F. J. (1999). Artificial immune systems: Part 1-Basic theory and applications (Tech. Rep. TR DCA 01/99). Retrieved December 2006, from ftp://ftp.dca.fee.unicamp.br/pub/docs/vonzuben/Inunes
109. De Castro, L. N., & von Zuben, F. J. (2000). Artificial immune systems: Part 2-A survey of applications (Tech. Rep. TR DCA 02/00). Retrieved December 2006, from ftp://ftp.dca.fee.unicamp.br/pub/docs/vonzuben/Inunes
110. Fudenberg, D., & Tirole, J. (1991). Game theory. Boston: MIT Press.
111. Hofbauer, J., & Sigmund, K. (1998). Evolutionary games and population dynamics. Cambridge University Press.
112. Jerne, N. K. (1974). Towards a network theory of the immune system. Annals of Immunology, 125C, 373-389.
113. Benzatti, D. (2006). Emergent intelligence. Retrieved August 20, 2006, from http://ai-depot.com/CollectiveIntelligence/Ant-Colony.html
114. Deneubourg, J.L., Aron, S., Goss, S., & Pasteels, J.M. (1990). The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behaviour, 3(1), 159-168.
115. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.
116. Goss, S., Aron, S., Deneubourg, J. L., & Pasteels, J. M. (1989). Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76(12), 579-581.
117. McLean, A. (2004) Hunting down the player in a convincing manner. In S. Rabin (Ed.), AI Game Programming Wisdom 2.(pp. 151-159). Hingham, MA: Charles River Media
118. Moyson, F., & Manderick, B. (1988) The collective behaviour of ants: An example of self-organization in massive parallelism. Paper presented at the AAAI Spring Symposium on Parallel Models of Intelligence, Stanford, California.
119. Stentz, A. (1994). Optimal and efficient path planning for partially-known environments. Paper presented at the IEEE International Conference on Robotics and Automation (pp. 3310-3317), San Diego, California. IEEE
120. Bishop, C. M., Svensen, M., & Williams, C. K. I. (1998) The GTM: The generative topographic mapping. Neural Computation, 10(1), 215-234.
121. Bradley, J., & Hayes, G. (2005, April). Adapting reinforcement learning for computer games: Using group utility functions. In Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, Essex University, Colchester, Essex, U.K.
122. Fyfe, C. (2007). Two topographic maps for data visualization. Data Mining and Knowledge Discovery, 14(2), 207-224.
123. Likas, A. (1999). A reinforcement learning approach to on-line clustering. Neural Computation, 11, 1915-1932.
124. Sutton, R.S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
125. Tesauro, G. (1995). Temporal difference learning and TD-gammon. Communications of the ACM, 38(3).
126. Williams, R. J. (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.
127. Williams, R. J., & Peng, J. (1991). Function optimization using connectionist reinforcement learning networks. Connection Science, 3(3), 241-268.
128. Wolpert, D. H., & Lawson, J. W. (2002, July). Designing agent collectives for systems with Markovian dynamics. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy.
129. Beal, C., Beck, J., Westbrook, D., Atkin, M., & Cohen, P., (2002). Intelligent modeling of the user in interactive entertainment. Paper presented at the AAAI Spring Symposium on Artificial Intelligence and Interactive Entertainment (pp. 8-12), Stanford.
130. Brown, J.S., Burton, R.R., & Bell, A.G. (1975). SOPHIE: A step towards a reactive learning environment. International. Journal of Man-Machine. Studies, 7, 675-696.
131. Butcher, C., & Griesemer, J. (2002, March 21-23). The illusion of intelligence: The integration of AI and level design in Halo. Paper presented at the Game Developers Conference, San Jose, California.
132. Gilleade, K., & Dix, A. (2004, June). Using frustration in the design of adaptive videogames. In Proceedings of ACE 2004, Advances in Computer Entertainment Technology. ACM Press.
133. Houlette, R. (2004). Player modeling for adaptive games. In S. Rabin (Ed.), AI game programming wisdom 2. Hingham, MA: Charles River Media, Inc.
134. Johnson, S. (2001). Emergence. Penguin Science.
135. Kuecklich, J. (2004, December). Other playings: Cheating in computer games. Paper presented at the Others Players Conference, IT University of Copenhagen, Denmark. Retrieved March 2005, from http://itu.dk/op/proceedings.htm
136. Laramée, F.D. (Ed.). Game design perspectives. Hingham, MA: Charles River Media.
137. Livingstone, D., & Charles, D. (2004, July 25-26). Intelligent interfaces for digital games. Paper presented at the AAAI-04 Workshop on Challenges in Game AI.
138. Manslow, J. (2002), Learning and adaptation in games. In S. Rabin (Ed.), AI game programming wisdom (pp. 557-566). Hingham, MA: Charles River Media.
139. Miller, S. (2004). Auto-dynamic difficulty (Website forum debate). Retrieved February 22, 2006, from http://dukenukem.typepad.com/game_matters/2004/01/autoadjusting_g.html
140. Poole, S. (2004, August). On beginnings. Edge Magazine, 139, 24. U.K.: Future Publishing.
141. Poole, S. (2005, March). Snake in the grass. Edge Magazine 147, 120. U.K.: Future Publishing.
142. Redeye. (2004, August). Content with the playgrounds. Edge Magazine, 139, 22. U.K.: Future Publishing.
143. Rouse, R., III (2001). Game design: Theory and practice. Wordware Publishing.
144. Sykes, J., & Brown, S., (2003). Affective gaming: Measuring emotion through the gamepad. Human Factors in Computing, pp 732-733 CHI 2003.
145. Tsymbal, A. (2004). The problem of concept drift: Definitions and related work (Tech. Rep. TCD-CS-2004-15). Trinity College Dublin, Department of Computer Science, Ireland.
146. Aha, D. (2005, July). TIELT: Testbed for entegrating and evaluating learning techniques. Paper presented at the TIELT, AAAI'05 Intelligent Systems Demonstrations.
147. Akman, V., & Blackburn, P. (2000). Editorial: Alan Turing and artificial intelligence. Journal of Logic, Language and Information, 9, 391-395.
148. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence Journal, 47, 139-159.
149. Butcher, C., & Griesemer, J. (2002, March 21-23). The illusion of intelligence: The integration of AI and level design in Halo. Paper presented at the Game Developers Conference, San Jose, California.
150. Charles, D. (2003, November 2-6). Enhancing gameplay: Challenges for artificial intelligence in digital games. Paper presented at the Level Up: Digital Games Research Conference, Utrecht.
151. Cohen, P. (2004, July 25-29). If not the Turing test, then what? Paper presented at the Nineteenth National Conference on Artificial Intelligence. American Association of Artificial Intelligence.
152. Colby, K. M. (1981). Modeling a paranoid mind. Behavioral and Brain Sciences, 4, 515-560.
153. Dick, P. K. (1968). Do androids dream of electric sheep?
154. Dick, P. K., Fancher H., Peoples, D., & Scott, R. (Director). (1982). Blade runner. [Motion Picture].
155. Harnad, S. (2000). Minds, machines and Turing. Journal of Logic, Language and Information, 9, 425-445.
156. Hayes, P. J., & Ford, K. M. (1995). Turing test considered harmful. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal.
157. Humphrys, M. (1995). How my program passed the Turing Test. Retrieved January 12, 2007, from http://www.computing.dcu.ie/~humphrys/eliza.html
158. Krol, M. (1999). Have we witnessed a real life Turing Test? IEEE Computer, (March), 27-30.
159. Laird, J. E., & Duchi, J. C. (2000, November 3-5). Creating human-like synthetic characters with multiple skill levels: A case study using the Soar Quakebot. Paper presented at the AAAI 2000 Fall Symposium: Simulating Human Agents, North Falmouth, Massachusetts.
160. Laird, J. E., & van Lent, M. (1999, November 3-5). Developing an artificial intelligence engine. In Proceedings of the Game Developers Conference, San Jose, California.
161. Livingstone, D. (2006). Turing's test and believable AI in games. Computers in Entertainment, 4(1). Retrieved March 2006, from http://www.acm.org/pubs/cie.html.
162. MacDorman, K. F., & Ishiguro, H. (2006). The uncanny advantage of using androids in social and cognitive science research. Interaction Studies, 7(3), 297-337.
163. Mac Namee, B. (2004). Proactive persistent agents: Using situational intelligence to create support characters in character-centric computer games. Unpublished doctoral thesis, University of Dublin, Department of Computer Science.
164. th International Conference on Intelligent Games and Simulation, London.
165. McGlinchey, S., & Livingstone, D. (2004). What believability testing can tell us. Paper presented at CGAIDE, Microsoft Campus, Reading.
166. Purdy, J. H., & Thornton, J. S. (2004, August). What effects should a game have on brain & body to be successful? Paper presented at the Game Developers Conference Europe, London.
167. Searle, J. R. (1980). Minds, brains and programs. Behavioral and Brain Sciences, 3, 417-424.
168. Sweetser, P., & Drenna, P. (2003, November 20). User-centred design in games. Paper presented at the Australian Game Developers Conference Academic Summit, Melbourne, Australia.
169. Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX (236), 433-460.
170. Westwood. (1998). Blade Runner (PC CD-Rom).
171. Wetzel, B. (2004, July). Step one: Document the problem. Paper presented at AAAI Workshop on Challenges in Game AI, San Jose, California.