Warianty tytułu
Języki publikacji
Abstrakty
This paper outlines the primary steps to investigate if artificial agents can be considered as true substitutes of humans. Based on a Socially augmented microworld (SAM) human tracking behavior was analyzed using time series. SAM involves a team of navigators jointly steering a driving object along different virtual tracks containing obstacles and forks. Speed and deviances from track are logged, producing high-resolution time series of individual (training) and cooperative tracking behavior. In the current study 52 time series of individual tracking behavior on training tracks were clustered according to different similarity measures. Resulting clusters were used to predict cooperative tracking behavior in fork situations. Results showed that prediction was well for tracking behavior shown at the first and, moderately well at the third fork of the cooperative track: navigators switched from their trained to a different tracking style and then back to their trained behavior. This matches with earlier identified navigator types, which were identified on visual examination. Our findings on navigator types will serve as a basis for the development of artificial agents, which can be compared later to behavior of human navigators.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
133--147
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
- Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg D. Obradovica 4, 21000 Novi Sad, Serbia, kurba@dmi.uns.ac.rs
autor
- Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg D. Obradovica 4, 21000 Novi Sad, Serbia, mira@dmi.uns.ac.rs
autor
- Institute of Psychology, Humboldt University Berlin, Rudower Chaussee 18, 12489 Berlin, Germany, charlotte.bernstorff@hu-berlin.de
autor
- Institute of Psychology, Humboldt University Berlin, Rudower Chaussee 18, 12489 Berlin, Germany, jens.nachtwei@hu-berlin.de
autor
- Institute of Informatics, Humboldt University Berlin, Rudower Chaussee 25, 12489 Berlin, Germany, hdb@informatik.hu-berlin.de
Bibliografia
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- [2] Azevedo, J., Almeida, R., Almeida, P.: Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review, International Journal of Intelligence Science, 2(4A), 2012, 176–180.
- [3] Badica, C., Budimac, Z., Burkhard, H.-D., Ivanović, M.: Software Agents: languages, tools, platforms, Computer Science and Information Systems, 8(2), 2011, 255–296.
- [4] Berndt, D. J., Clifford, J.: Using Dynamic Time Warping to Find Patterns in Time Series, KDD Workshop,1994.
- [5] Brehmer, B., Dörner, D.: Experiments with computer-simulated microworlds: Escaping both the narrow straits of the laboratory and the deep blue sea of the field study, Computers in Human Behavior, 9(23), 1993,171 – 184, ISSN 0747-5632.
- [6] Burkhard, H.-D., Jahn, L., Kain, S., Meyer, C., Muetterlein, J., Nachtwei, J., Niestroj, N., Rougk, S., Schneider, M.: Artificial Subjects in the Psychological Experiment ”Socially Augmented Microworld (SAM)”, Proceedings of Concurrency, Specification, and Programming, 2011.
- [7] Chapman, T., Nettelbeck, T., Welsh, M., Mills, V.: Investigating the construct validity associated with microworld research: A comparison of performance under different management structures across expert and non-expert naturalistic decision-making groups, Australian Journal of Psychology, 58(1), 2006, 40–47.
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- [10] Chen, Q., Hu, G., Gu, F., Xiang, P.: Learning optimal warping window size of DTW for time series classification,11th International Conference on Information Science, Signal Processing and their Applications(ISSPA), 2012.
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- [13] Fan, X., Yen, J.: Modeling and Simulating Human Teamwork Behaviors Using Intelligent Agents, In Journal of Physics of Life Reviews, 1, 2004, 173–201.
- [14] Feng, X., Huang, H.: A fuzzy-set-based Reconstructed Phase Space method for identification of temporal patterns in complex time series, Knowledge and Data Engineering, IEEE Transactions on, 17(5), 2005,601–613, ISSN 1041-4347.
- [15] Funke, J.: Computer-based testing and training with scenarios from complex problem-solving research: Advantages and disadvantages, 1998.
- [16] Gonzalez, C., Vanyukov, P., Martin, M. K.: The use of microworlds to study dynamic decision making, Computers in Human Behavior, 21, 2005, 273–286.
- [17] Gray, W. D.: Simulated task environments: The role of high-fidelity simulations, scaled worlds, synthetic environments, and laboratory tasks in basic and applied cognitive research, Cognitive Science Quarterly,2(2), 2002, 205–227.
- [18] Han, J.: Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005, ISBN 1558609016.
- [19] Itakura, F.: Minimum prediction residual principle applied to speech recognition, Acoustics, Speech and Signal Processing, IEEE Transactions on, 23(1), 1975, 67–72, ISSN 0096-3518.
- [20] Jobidon, M.-E., Breton, R., Rousseau, R., Tremblay, S.: Team Response to Workload Transition: The Role of Team Structure, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(17), 2006,1769–1773.
- [21] Johnson, B.: Algorithmic Trading and DMA: An introduction to direct access trading strategies, Myeloma Press, February 2010, ISBN 0956399207.
- [22] Keogh, E.: A decade of progress in indexing and mining large time series databases, Proceedings of the 32ndinternational conference on Very large data bases, VLDB ’06, VLDB Endowment, 2006.
- [23] Kurbalija, V., Radovanović, M., Geler, Z., Ivanović, M.: A framework for time-series analysis, Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications,AIMSA’10, Springer-Verlag, Berlin, Heidelberg, 2010, ISBN 3-642-15430-1, 978-3-642-15430-0.
- [24] Kurbalija, V., Radovanović, M., Geler, Z., Ivanović, M.: The Influence of Global Constraints on DTW and LCS Similarity Measures for Time-Series Databases, in: Third International Conference on Software, Services and Semantic Technologies S3T 2011 (D. Dicheva, Z. Markov, E. Stefanova, Eds.), vol. 101 of Advances in Intelligent and Soft Computing, Springer Berlin Heidelberg, 2011, ISBN 978-3-642-23162-9,67–74.
- [25] Nakkeeran, K., Garla, S., Chakraborty, G.: Application of Time Series Clustering using SAS Enterprise Miner for a Retail Chain, 2012, Poster presented at SAS Global Forum, Florida.
- [26] Ratanamahatana, C. A., Keogh, E. J.: Three Myths about Dynamic Time Warping Data Mining., SDM, 2005.
- [27] Ratanamahatana, C. A., Lin, J., Gunopulos, D., Keogh, E. J., Vlachos, M., Das, G.: Mining Time Series Data, in: Data Mining and Knowledge Discovery Handbook, 2010, 1049–1077.
- [28] Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition, Acoustics, Speech and Signal Processing, IEEE Transactions on, 26(1), 1978, 43–49, ISSN 0096-3518.
- [29] Schubert, S., Lee, T.: Time Series Data Mining with SAS Enterprise Miner, 2011, Proceedings of the SAS Global Forum 2011 Conference. Cary, NC: SAS Institute Inca.
- [30] Seifert, J. W.: Data Mining: an overview, 2004, In CRS report for Congress, December 16.
- [31] Simoudis, E.: Reality check for data mining, IEEE Expert, 11(5), 1996, 26–33, ISSN 0885-9000.
- [32] Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories, Data Engineering, 2002. Proceedings. 18th International Conference on, 2002, ISSN 1063-6382.
- [33] Wandke, H., Nachtwei, J.: The different human factor in automation: the developer behind vs. the operator inaction, in: Human Factors for assistance and automation (D. deWaard, F. Flemisch, B. Lorenz, H. Oberheid, K. Brookhuis, Eds.), Shaker Publishing, 2008.
- [34] Xi, X., Keogh, E., Shelton, C.,Wei, L., Ratanamahatana, C. A.: Fast time series classification using numerosity reduction, Proceedings of the 23rd international conference on Machine learning, ICML ’06, ACM, NewYork, NY, USA, 2006, ISBN 1-59593-383-2.
- [35] Zhang, X., Xiao, W.-x.: Clustering based Two-Stage Text Classification Requiring Minimal Training Data,Computer Science and Information Systems, 9(4), 2012, 1627–1644.
- [36] Zhang, Y., Jansen, B. J., Spink, A.: Time series analysis of a Web search engine transaction log, Inf. Process. Manage., 45(2), March 2009, 230–245, ISSN 0306-4573.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-5cf552eb-cd44-4999-ad0c-f8ccbf3615b5