Learning alternative ways of performing a task

[EN] A common way of learning to perform a task is to observe how it is carried out by experts. However, it is well known that for most tasks there is no unique way to perform them. This is especially noticeable the more complex the task is because factors such as the skill or the know-how of the expert may well affect the way she solves the task. In addition, learning from experts also suffers of having a small set of training examples generally coming from several experts (since experts are usually a limited and ex- pensive resource), being all of them positive examples (i.e. examples that represent successful executions of the task). Traditional machine learning techniques are not useful in such scenarios, as they require extensive training data. Starting from very few executions of the task presented as activity sequences, we introduce a novel inductive approach for learning multiple models, with each one representing an alter- native strategy of performing a task. By an iterative process based on generalisation and specialisation, we learn the underlying patterns that capture the different styles of performing a task exhibited by the examples. We illustrate our approach on two common activity recognition tasks: a surgical skills training task and a cooking domain. We evaluate the inferred models with respect to two metrics that measure how well the models represent the examples and capture the different forms of executing a task showed by the examples. We compare our results with the traditional process mining approach and show that a small set of meaningful examples is enough to obtain patterns that capture the different strategies that are followed to solve the tasks.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN2014-61716-EXP (SUPERVASION) and RTI2018-094403-B-C32, and by Generalitat Valenciana under grant PROMETEO/2019/098. David Nieves is also supported by the Spanish MINECO under FPI grant (BES-2016-078863).Nieves, D.; Ramírez Quintana, MJ.; Montserrat Aranda, C.; Ferri Ramírez, C.; Hernández-Orallo, J. (2020). Learning alternative ways of performing a task. Expert Systems with Applications. 148:1-18. https://doi.org/10.1016/j.eswa.2020.113263118148van der Aalst, W. M., Bolt, A., & van Zelst, S. J. (2017). Rapidprom: Mine your processes and not just your data. arXiv:1703.03740.Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713-732. doi:10.1016/j.is.2006.05.003Adé, H., de Raedt, L., & Bruynooghe, M. (1995). Declarative bias for specific-to-general ILP systems. Machine Learning, 20(1-2), 119-154. doi:10.1007/bf00993477Ahmidi, N., Tao, L., Sefati, S., Gao, Y., Lea, C., Haro, B. B., … Hager, G. D. (2017). A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery. IEEE Transactions on Biomedical Engineering, 64(9), 2025-2041. doi:10.1109/tbme.2016.2647680Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Action understanding as inverse planning. Cognition, 113(3), 329-349. doi:10.1016/j.cognition.2009.07.005Blum, T., Padoy, N., Feußner, H., & Navab, N. (2008). Workflow mining for visualization and analysis of surgeries. International Journal of Computer Assisted Radiology and Surgery, 3(5), 379-386. doi:10.1007/s11548-008-0239-0Camacho, R., Carreira, P., Lynce, I., & Resendes, S. (2014). An ontology-based approach to conflict resolution in Home and Building Automation Systems. Expert Systems with Applications, 41(14), 6161-6173. doi:10.1016/j.eswa.2014.04.017Caruana, R. (1997). Machine Learning, 28(1), 41-75. doi:10.1023/a:1007379606734Liming Chen, Hoey, J., Nugent, C. D., Cook, D. J., & Zhiwen Yu. (2012). Sensor-Based Activity Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 790-808. doi:10.1109/tsmcc.2012.2198883Chen, L., Nugent, C. D., & Wang, H. (2012). A Knowledge-Driven Approach to Activity Recognition in Smart Homes. IEEE Transactions on Knowledge and Data Engineering, 24(6), 961-974. doi:10.1109/tkde.2011.51Chen, Y., Wang, J., Huang, M., & Yu, H. (2019). Cross-position activity recognition with stratified transfer learning. Pervasive and Mobile Computing, 57, 1-13. doi:10.1016/j.pmcj.2019.04.004Cook, D., Feuz, K. D., & Krishnan, N. C. (2013). Transfer learning for activity recognition: a survey. Knowledge and Information Systems, 36(3), 537-556. doi:10.1007/s10115-013-0665-3Dai, P., Di, H., Dong, L., Tao, L., & Xu, G. (2008). Group Interaction Analysis in Dynamic Context. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(1), 275-282. doi:10.1109/tsmcb.2007.909939Ding, R., Li, X., Nie, L., Li, J., Si, X., Chu, D., … Zhan, D. (2018). Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition. Sensors, 19(1), 57. doi:10.3390/s19010057Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009). Efficient duration and hierarchical modeling for human activity recognition. Artificial Intelligence, 173(7-8), 830-856. doi:10.1016/j.artint.2008.12.005Fürnkranz, J. (1999). Artificial Intelligence Review, 13(1), 3-54. doi:10.1023/a:1006524209794Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining. ACM Computing Surveys, 38(3), 9. doi:10.1145/1132960.1132963Hoey, J., Poupart, P., Bertoldi, A. von, Craig, T., Boutilier, C., & Mihailidis, A. (2010). Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Computer Vision and Image Understanding, 114(5), 503-519. doi:10.1016/j.cviu.2009.06.008Hong, J., Suh, E., & Kim, S.-J. (2009). Context-aware systems: A literature review and classification. Expert Systems with Applications, 36(4), 8509-8522. doi:10.1016/j.eswa.2008.10.071Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2017). Imitation Learning. ACM Computing Surveys, 50(2), 1-35. doi:10.1145/3054912Kalra, L., Zhao, X., Soto, A. J., & Milios, E. (2013). Detection of daily living activities using a two-stage Markov model. Journal of Ambient Intelligence and Smart Environments, 5(3), 273-285. doi:10.3233/ais-130208Kardas, K., & Cicekli, N. K. (2017). SVAS: Surveillance Video Analysis System. Expert Systems with Applications, 89, 343-361. doi:10.1016/j.eswa.2017.07.051Krüger, F., Nyolt, M., Yordanova, K., Hein, A., & Kirste, T. (2014). Computational State Space Models for Activity and Intention Recognition. A Feasibility Study. PLoS ONE, 9(11), e109381. doi:10.1371/journal.pone.0109381Neumann, A., Elbrechter, C., Pfeiffer-Leßmann, N., Kõiva, R., Carlmeyer, B., Rüther, S., … Ritter, H. J. (2017). «KogniChef»: A Cognitive Cooking Assistant. KI - Künstliche Intelligenz, 31(3), 273-281. doi:10.1007/s13218-017-0488-6Papadimitriou, P., Dasdan, A., & Garcia-Molina, H. (2010). Web graph similarity for anomaly detection. Journal of Internet Services and Applications, 1(1), 19-30. doi:10.1007/s13174-010-0003-xPeng, L., Chen, L., Ye, Z., & Zhang, Y. (2018). AROMA. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(2), 1-16. doi:10.1145/3214277Rosen, J., Solazzo, M., Hannaford, B., & Sinanan, M. (2002). Task Decomposition of Laparoscopic Surgery for Objective Evaluation of Surgical Residents’ Learning Curve Using Hidden Markov Model. Computer Aided Surgery, 7(1), 49-61. doi:10.3109/10929080209146016Sadilek, A., & Kautz, H. (2012). Location-Based Reasoning about Complex Multi-Agent Behavior. Journal of Artificial Intelligence Research, 43, 87-133. doi:10.1613/jair.3421Sanchez, D., Tentori, M., & Favela, J. (2008). Activity Recognition for the Smart Hospital. IEEE Intelligent Systems, 23(2), 50-57. doi:10.1109/mis.2008.18Škrjanc, I., Andonovski, G., Ledezma, A., Sipele, O., Iglesias, J. A., & Sanchis, A. (2018). Evolving cloud-based system for the recognition of drivers’ actions. Expert Systems with Applications, 99, 231-238. doi:10.1016/j.eswa.2017.11.008Sun, X., Kashima, H., & Ueda, N. (2013). Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning. IEEE Transactions on Knowledge and Data Engineering, 25(11), 2551-2563. doi:10.1109/tkde.2012.246Twomey, N., Diethe, T., Kull, M., Song, H., Camplani, M., Hannuna, S., et al. (2016). The sphere challenge. arXiv:1603.00797.Voulodimos, A., Kosmopoulos, D., Vasileiou, G., Sardis, E., Anagnostopoulos, V., Lalos, C., … Varvarigou, T. (2012). A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments. IEEE Multimedia, 19(3), 42-52. doi:10.1109/mmul.2012.31Wallace, C. S. (1999). Minimum Message Length and Kolmogorov Complexity. The Computer Journal, 42(4), 270-283. doi:10.1093/comjnl/42.4.27