Online learning of event definitions

Katzouris, Nikos; Artikis, Alexander; Παλιούρας, Γεώργιος

doi:10.1017/s1471068416000260

Cited by 27 publications

(46 citation statements)

References 33 publications

(57 reference statements)

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“…The second level contains CEs, describing the activities between multiple persons and/or objects, i.e., people meeting and moving together, leaving an object and fighting. Similar to earlier work (Skarlatidis et al, 2015;Katzouris et al, 2016), we focus on the meet and move CEs, and from the 28 videos, we extract 19 sequences that contain annotation for these CEs. shown that compressing vessel trajectories in this way allows for accurate trajectory reconstruction, while at the same time improving stream reasoning times significantly (Patroumpas et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Semi-supervised online structure learning for composite event recognition

2019

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Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.

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Section: Methodsmentioning

confidence: 99%

Semi-supervised online structure learning for composite event recognition

2019

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“…We are implementing RTEC in Scala in order to pave the way for distributed CER. Additionally, we are developing machine learning techniques for continuously refining patterns given new data streaming into the system [11,12]. Finally, in the context of the EU-funded INFORE project, we are integrating satellite images with position signals and geographical information for a more complete account of maritime situational awareness.…”

Section: Summary and Further Workmentioning

confidence: 99%

Composite Event Recognition for Maritime Monitoring

Pitsikalis

Artikis

Dreo

et al. 2019

Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems

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Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.

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“…Another situation where scalability is needed, is when there is a single but large example. Works in (Katzouris et al 2015;Katzouris et al 2017) talk about this situation. Our work is also related to the work in logical vision (Dai et al 2015) that aims to learn symbolic representation of simple geometric concepts.…”

Section: Related Workmentioning

confidence: 99%

Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Mitra

Baral²

2018

Theory and Practice of Logic Programming

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Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available athttps://goo.gl/KdWAcV.

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Online learning of event definitions

Cited by 27 publications

References 33 publications

Semi-supervised online structure learning for composite event recognition

Semi-supervised online structure learning for composite event recognition

Composite Event Recognition for Maritime Monitoring

Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

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