Novel Machine learning approach for Self-Aware prediction based on the Contextual reasoning



self-awareness, threat assessment, contextual knowledge, contextual reasoning, Decision Tree, Support Vector Machine, OPTICS


Machine learning is compelling in solving various applied problems. Nevertheless, machine learning methods lack the contextual reasoning capabilities and cannot be fitted to utilize additional information about circumstances, environments, backgrounds, etc. Such information provides essential knowledge about possible reasons for particular actions. This knowledge could not be processed directly by either machine learning methods. This paper presents the context-aware machine learning approach for actor behavior contextual reasoning analysis and context-based prediction for threat assessment.
Moreover, the proposed approach uses context-aware prediction to tackle the interaction between actors. An idea of the technique lies in the cooperative use of two classification methods when one way predicts an actor’s behavior. The second method discloses such predicted action (behavior) that is non-typical or unusual. Such integration of two-method allows the actor to make the self-awareness threat assessment based on relations between different actors where some multidimensional numerical data define the connections. This approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. The suggested approach is based on the Decision Tree and Support Vector Method algorithm. Due to the complexity of context, marine traffic data was chosen to demonstrate the proposed approach capability. This technique could deal with the end-to-end approach for safe vessel navigation in maritime traffic with considerable ship congestion.


[1] Rosenfield, I., Clancey, W.J. (1991). Book review "The Invention of Memory: A New View of the Brain", Artificial Intelligence, 50, 241—284 (1991).

[2] Brezillon, P., Pomerol, J.-C. (1999). Contextual knowledge and proceduralized context, In: AAAI Workshop on Modeling Context in AI Applications, Paris. 16—20 (1999).

[3] The International Regulations for Preventing Collisions at Sea 1972 (COLREGS). International Maritime Organization (1972), 1972.

[4] Fan, Y., Sun, X., Wang, G. (2019). An autonomous dynamic collision avoidance control method for unmanned surface vehicle in unknown ocean environment, International Journal of Advanced Robotic Systems, 16 (2019).

[5] Geng, X., Wang, Y., Wang, P., Zhang, B. (2019). Motion plan of maritime autonomous surface ships by dynamic programming for collision avoidance and speed optimization, Sensors, 19 (2019).

[6] Ahn, J.H., Rhee, K.P., You, Y.J. (2012). A study on the collision avoidance of a ship using neural networks and fuzzy logic, Applied Ocean Research, 37, 162—173 (2012).

[7] Dixena, D., Chakraborty, B., Debnath, N. (2011). Application of Case-based reasoning for ship turning emergency to prevent collision, In: 2011 9th IEEE International Conference on Industrial Informatics, Lisbon, IEEE. 654—659. (2011).

[8] Bukhari, A.C., Tusseyeva, I., Lee, B.G., Kim, Y.G. (2013). An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system, Expert Systems with Applications, 40, 1220—1230 (2013).

[9] Daranda, A., Dzemyda, G. (2020). Navigation decision support: Discover of vessel traffic anomaly according to the historic marine data, International Journal of Computers, Communications and Control, 15, 3, 3864 (2020).

[10] Papenhuijzen, R., Stassen, H.G. (1992). Fuzzy set theory for modelling the navigator’s behavior, IFAC Proceedings Volumes, 25, 53—59 (1992).

[11] Westrenen, F. V. (1995). Towards a decision making model of river pilots, IFAC Proceedings Volumes, 28, 217—222 (1995).

[12] Nicolau, V., Aiordachioaie, D., Popa, R. (2004). Neural network prediction of the wave influence on the yaw motion of a ship, In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Budapest, IEEE. pp. 2801—2806 (2004)

[13] Haris, S., Amdahl, J. (2013). Analysis of ship—ship collision damage accounting for bow and side deformation interaction, Marine Structures, 32, 18—48 (2013).

[14] Xiao, Z., Ponnambalam, L., Fu, X., Zhang, W. (2017). Maritime traffic probabilistic forecasting based on vessels’ waterway patterns and motion behaviors, IEEE Transactions on Intelligent Transportation Systems, 18, 3122—3134 (2017).

[15] Kim, J.S. (2017). Vessel target prediction method and Dead Reckoning Position based on SVR seaway model, International Journal of Fuzzy Logic and Intelligent Systems, 17, 279—288 (2017).

[16] Kim, J.S., Jeong, J.S. (2017). Extraction of reference seaway through machine learning of ship navigational data and trajectory, International Journal of Fuzzy Logic and Intelligent Systems, 17, 82—90 (2017).

[17] Daranda, A. (2016). Neural network approach to predict marine traffic, Baltic J. Modern Computing, 4, 3, 483—495 (2016)

[18] Ester, M., Kriegel, H.-P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, KDD’96., pp. 226—231 (1996)

[19] Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J. (1999). OPTICS: ordering points to identify the clustering structure, SIGMOD Record, 28, 49—60 (1999).

[20] Quinlan, J.R. (1986). Induction of decision trees, Machine Learning, 1, 81—106 (1986).

[21] Wang, X., Chen, B., Qian, G., Ye, F. (2000). On the optimization of fuzzy decision trees, Fuzzy Sets and Systems, 112, 117—125 (2000).

[22] Murthy, S.K., Kasif, S., Salzberg, S. (1994). A system for induction of oblique decision trees, Journal of Artificial Intelligence Research, 2, 1—32 (1994).

[23] Sokolov, E.N. (1960). Neuronal models and the orienting reflex, In: Mary A.B. Brazier (ed.) The Central Nervous System and Behavior, New York, Macey. pp. 187—276. (1960).

[24] Markou, M., Singh, S. (2003). Novelty detection: A review - Part 1: Statistical approaches, Signal Processing, 83, 2481—2497 (2003).

[25] Markou, M., Singh, S. (2003). Novelty detection: A review - Part 2:: Neural network based approaches, Signal Processing, 83, 2499—2521 (2003).

[26] Schí¶lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C. (2001). Estimating the support of a high-dimensional distribution, Neural Computation, 13, 1443—1471 (2001).

[27] Pedregosa, F., Grisel, O., Weiss, R., Passos, A., Brucher, M., Varoquax, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Brucher, M. (2011). Scikit-learn: Machine learning in Python, Journal of Machine Learning Research , 12, 2825—2830 (2011).

Additional Files



Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.