Abstract
In this work, we describe a promising approach to harnessing human computation in mainstream video games. Our hypothesis is that one of the best approaches to seamlessly incorporating harnessing withing these games is by examining existing game mechanics and matching them to meta-heuristic algorithms. In particular, we believe that the best choices for early exploration of this problem are nature inspired meta-heuristic algorithms for combinatorial optimization problems. In this paper, we will describe the problem in more detail and describe two proof of concept games that demonstrate the viability of this approach. The first game is designed to be incorporated in Real-time Strategy games within the resource gathering aspects of these games, and the algorithm and problem that are used is related to Ant Colony Optimization and the Traveling Salesman Problem. The second game explores a racing game where the problem and algorithm are embedded in the numerical characteristics of the racer such as speed, agility, and jump power. These characteristics represent current solutions to different traveling salesman problems, and the solutions are modified through training and mating of racers; this is analogous to mutations and crossbreeding in genetic algorithms.
Chapter PDF
Similar content being viewed by others
Keywords
- Travel Salesman Problem
- Travel Salesman Problem
- Combinatorial Optimization Problem
- Metaheuristic Algorithm
- Pheromone Level
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35, 268–308 (2003), http://doi.acm.org/10.1145/937503.937505
Cusack, C., Largent, J., Alfuth, R., Klask, K.: Online games as social-computational systems for solving np-complete problems. In: Meaningful Play (2010)
Das, S., Suganthan, P.: Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Tech. rep. (2010), http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/SharedDocuments/CEC2011-RWP/Tech-Rep.pdf
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1), 29–41 (1996), http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=484436&tag=1
ESP Game: (2008), http://www.gwap.com/gwap/gamesPreview/espgame/
Falstein, N.: Natural Funativity. Gamasutra.com (2004), http://www.gamasutra.com/features/20041110/falstein_pfv.html
FoldIt: (2008) http://fold.it/portal/
Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13, 533–549 (1986), http://dl.acm.org/citation.cfm?id=15310.15311
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Addison-Wesley Professional (January 1989), http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0201157675
Internet Eyes: (2009), http://interneteyes.co.uk/
Khatib, F., Cooper, S., Tyka, M.D., Xu, K., Makedon, I., Popovic, Z., Baker, D., Players, F.: Algorithm discovery by protein folding game players. Proceedings of the National Academy of Sciences (2011), http://www.ts-si.org/files/doi101073pnas1115898108.pdf
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)
Kotovsky, K., Hayes, J.R., Simon, H.A.: Tower of Hanoi - Problem Isomorphs and Solution Processes. Bulletin of the Psychonomic Society 22(4), 290 (1984)
Macchiarella, P.: Trends in Digital Gaming: Free-to-Play, Social, and Mobile Games. Tech. rep. (2012)
Papadimitriou, C.H., Steiglitz, K.: Optimization Algorithms and Complexity. Dover Publications, Inc., New York (1982)
Pham, D., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm, a novel tool for complex optimisation problems. In: Virtual International Conference on Intelligent Production Machines and Systems, pp. 454–459 (2006), http://www.bees-algorithm.com/modules/2/4.pdf
Quinn, A.J., Bederson, B.B.: Human computation: a survey and taxonomy of a growing field. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1403–1412 (2011), http://doi.acm.org/10.1145/1978942.1979148
Simon, H.A., Hayes, J.R.: Understanding Process - Problem Isomorphs. Cognitive Psychology 8(2), 165–190 (1976)
Stradust@Home: (2009), http://stardustathome.ssl.berkeley.edu/
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Search, REPORT SFI-TR-95-02-010. Tech. rep. (1996)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)
Zhang, J., Johnson, T., Wang, H.: Isomorphic Representations Lead to the Discovery of Different Forms of a Common Strategy with Different Degrees of Generality. In: Proceedings of the 20th Annual Conference of the Cognitive Science Society (1998), http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=93CB1A5E27B562C8B7BD109A1BF3A241?doi=10.1.1.139.9596&rep=rep1&type=pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jamieson, P., Grace, L., Hall, J., Wibowo, A. (2013). Metaheuristic Entry Points for Harnessing Human Computation in Mainstream Games. In: Ozok, A.A., Zaphiris, P. (eds) Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol 8029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39371-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-39371-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39370-9
Online ISBN: 978-3-642-39371-6
eBook Packages: Computer ScienceComputer Science (R0)