Abstract
Most event recognition approaches in sensor environments are based on manually constructed patterns for detecting events, and lack the ability to learn relational structures in the presence of uncertainty. We describe the application of \(\mathtt {OSL}\alpha \), an online structure learner for Markov Logic Networks that exploits Event Calculus axiomatizations, to event recognition for traffic management. Our empirical evaluation is based on large volumes of real sensor data, as well as synthetic data generated by a professional traffic micro-simulator. The experimental results demonstrate that \(\mathtt {OSL}\alpha \) can effectively learn traffic congestion definitions and, in some cases, outperform rules constructed by human experts.
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Funded by EU FP7 project SPEEDD (619435).
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Michelioudakis, E., Artikis, A., Paliouras, G. (2017). Online Structure Learning for Traffic Management. In: Cussens, J., Russo, A. (eds) Inductive Logic Programming. ILP 2016. Lecture Notes in Computer Science(), vol 10326. Springer, Cham. https://doi.org/10.1007/978-3-319-63342-8_3
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DOI: https://doi.org/10.1007/978-3-319-63342-8_3
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