LSA-BASED SEMANTIC REPRESENTATION OF ACTION GAMES

Katia Lida Kermanidis, Kostas Anagnostou

2010

Abstract

Modeling the semantic space of a complex dynamic domain, like an action game, by automatically identifying the relations governing the game’s concepts, entities, actions and other features, is a challenging research objective. In this paper we propose modeling the semantic space of the action game SpaceDebris, in order to identify semantic similarities between players’ gaming styles. To this end we employ Latent Semantic Analysis and attempt to identify latent underlying semantic information governing the various gaming techniques. The several challenging research issues that arise when attempting to apply Latent Semantic Analysis to non-textual data describing a complex dynamic problem space (defining the semantic vocabulary and “word” utterances, deciding upon the dimensionality reduction rate, etc.) are addressed, and the framework of the proposed experimental setup is described. The extracted similarities are further employed for player modelling, i.e. grouping players according to their playing styles.

References

  1. Anagnostou, K., Maragoudakis, M., 2009. Data mining for player modeling in videogames. In Panhellenic Conference on Informatics (PCI). Corfu, Greece.
  2. Basili, R., Petitti, R., Saracino, D., 2007. LSA-based automatic acquisition of semantic image descriptions. In Conference on Semantics and Digital Media Technologies, LNCS 4816, pp. 41-55.
  3. Done, B., Khatri, P., Done, A., Draghici, S., 2010. Predicting novel human gene ontology annotations using semantic analysis. In IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7(1), pp. 91-99.
  4. Dong, Q., Wang, X., Lin, L., 2006. Application of latent semantic analysis to protein remote homology detection. In Bioinformatics, vol. 22(3), pp. 285-290, Oxford University Press.
  5. Ganapathiraju, M., Balakrishnan, N., Reddy, R., KleinSeetharaman, J., 2005. Computational biology and language. In Ambient Intelligence for Scientific Discovery, LNAI 3345, pp. 25-47.
  6. Geisler, B., 2002. An empirical study of machine learning algorithms applied to modeling player behavior in a first person shooter video game. MSc Thesis, University of Wisconsin-Madison.
  7. Graesser, A. C., Penumatsa, P., Ventura, M., Cai, Z., Hu, X., 2007. Using LSA in AutoTutor: Learning through mixed-initiative dialogue in natural language. Handbook of Latent Semantic Analysis, T. Landauer, D. McNamara, S. Dennis, W. Kintsch Eds.
  8. Haley, D. T., Thomas, P., de Roeck, A., Petre, M., 2005. A research taxonomy for latent semantic analysisbased educational applications. In Conference on Recent Advances in Natural Language Processing. Borovets, Bulgaria.
  9. He, S., Du, J., Chen, H., Meng, J., Zhu, Q., 2008. Strategy-based player modeling during interactive entertainment sessions by using Bayesian classification. In 4th International Conference on Natural Computation (ICNC), pp.255-261.
  10. Hofmann, T., 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, vol. 22(1), pp. 89-115.
  11. Landauer, T., Foltz, P., Laham, D., 1998. An introduction to latent semantic analysis. In Discourse Processes, vol. 25, pp. 259-284.
  12. Lemaire, B., 1998. Models of high-dimensional semantic spaces. In 4th International Workshop on Multistrategy Learning (MSL 98).
  13. McCarthy, P. M., Briner, S., Rus, V., McNamara, D., 2007. Textual signatures: identifying text-types using latent semantic analysis to measure the cohesion of text structures, In A. Kao and S. Poteet (Eds), Natural Language Processing and Text Mining, pp. 107-122.
  14. Nakov, P., Valchanova, E., Angelova, G., 2003. Towards deeper understanding of the LSA performance. In Conference on Recent Advances in Natural Language Processing.
  15. Quesada, J. F., Kintsch, W., Gomez, E., 2001. A computational theory of complex problem solving using the vector space model (part I): latent semantic analysis, through the path of thousands of ants. In J. J. Cañas (Ed.), Cognitive Research with Microworlds, pp. 117-131, Granada, Spain.
  16. Roberts, D., Riedl, M., Isbell, C., 2007. Opportunities for machine learning to impact interactive narrative. In Workshop on Machine Learning and Games at NIPS.
  17. Shahine, G., Banerjee, B., 2007. Player modeling using knowledge transfer. In EUROSIS GAMEON-NA Conference, pp. 82-89. Gainesville, Florida, USA.
  18. Steinberger, J., Jezek, K., 2004. Using latent semantic analysis in text summarization and summary evaluation. In Conference on Information Systems, Implementation and Modeling (ISIM), pp. 93-100.
  19. Thawonmas, R., Ho, J., 2007. Classification of online game players using action transition probability and Kullback Leibler entropy. In Journal of Advanced Computational Intelligence and Intelligent Infromatics, Special issue on Advances in Intelligent Data Processing, vol. 11(3), pp. 319-326.
  20. Thue, D., Bulitko, V., Spetch, M., Wasylishen, E., 2007. Interactive storytelling: a player modelling approach. In 3rd International Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), pp. 43-48, Stanford, California, USA.
  21. van Oostendorp, H., Juvina, I., 2007. Using a cognitive model to generate web navigation support. In International Journal of Human-Computer Studies, vol. 65(10), pp. 887-897, Elsevier.
  22. Zampa, V., Lemaire, B., 2002. Latent semantic analysis for user modeling, In Journal of Intelligent Information Systems, vol. 18, pp. 15-30, Kluwer Academic Publishers.
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Paper Citation


in Harvard Style

Kermanidis K. and Anagnostou K. (2010). LSA-BASED SEMANTIC REPRESENTATION OF ACTION GAMES . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010) ISBN 978-989-8425-29-4, pages 218-223. DOI: 10.5220/0003082602180223


in Bibtex Style

@conference{keod10,
author={Katia Lida Kermanidis and Kostas Anagnostou},
title={LSA-BASED SEMANTIC REPRESENTATION OF ACTION GAMES},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)},
year={2010},
pages={218-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003082602180223},
isbn={978-989-8425-29-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)
TI - LSA-BASED SEMANTIC REPRESENTATION OF ACTION GAMES
SN - 978-989-8425-29-4
AU - Kermanidis K.
AU - Anagnostou K.
PY - 2010
SP - 218
EP - 223
DO - 10.5220/0003082602180223