IJCAI 2011 Tutorial: Logic-Based Event Recognition

 

Tutorial Description

Today's organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. This requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this tutorial we will review representative approaches of logic-based event recognition, and discuss open research issues of this field. The presentation of each approach will be structured as follows: representation, reasoning, and machine learning.

Throughout the tutorial we will illustrate the reviewed approaches using a real-world case study from the PRONTO project: event recognition for city transport management.

Moreover, we will give a demo of each approach, upon request, using one of the event recognition datasets that we have at our disposal.

Syllabus

The slides of the tutorial are available here. Note that this tutorial was also given in DEBS 2010.

A paper on the tutorial is available here.

Intended Audience

The intended audience of the tutorial consists of academics, students and practitioners investigating the open issues of event recognition, and/or willing to apply event recognition techniques for extracting knowledge from structured and unstructured datasets. Familiarity with Artificial Intelligence techniques is desirable.

Presenters

Alexander Artikis is a Research Associate in the Institute of Informatics & Telecommunications at NCSR "Demokritos", in Athens, Greece. He holds a PhD from Imperial College London on the topic of norm-governed multi-agent systems. His research interests lie in the areas of distributed artificial intelligence, temporal representation and reasoning, artificial intelligence & law, and description logics. He has published papers in related journals and conferences, such as the Artificial Intelligence Journal, the ACM Transactions on Computational Logic, and the Logic Journal of the IGPL. He is currently working on the EU FP7 PRONTO project, being responsible for the event recognition work-package. In the past he has worked for several international and national projects, including the highly successful EU FET ALFEBIITE project. Dr. Artikis has been teaching undergraduate courses on logic, and distributed artificial intelligence, in the University of Piraeus. He has served as a member of the program committees of several conferences and workshops, and has co-organised six workshops.

Georgios Paliouras is a senior researcher in the Institute of Informatics and Telecommunications at NCSR "Demokritos", in Athens, Greece. He holds a PhD from Manchester University on machine learning for event recognition. His research focuses on machine learning and knowledge discovery for ontology learning, user modeling, event recognition, information extraction and text classification. He is a member of the editorial board of the UMUAI journal and he has been participated in the organisation and programme committees of several conferences. He is also involved in many European and national research projects. Among others, he is responsible for NCSR "Demokritos" in the PRONTO project, where he contributes to event recognition and machine learning research. He has given a number of invited talks and tutorials at various institutions and conferences. He has taught postgraduate courses on Machine Learning and Information Extraction and has given lectures in numerous seminars and summer schools.

Francois Portet obtained his PhD in computing science at the University of Rennes 1 in 2005 where he stayed as a short-term lecturer until late 2006. In autumn 2006, he joined, as Research Fellow, the department of computing science of the University of Aberdeen. Since October 2008, he is associate Professor at the Grenoble Institute of Technology and at the Laboratoire d'Informatique de Grenoble. His research interests lie in the areas of temporal representation and reasoning, medical decision support systems, data mining, and reasoning with uncertainty in NLP. During his Ph.D at the IRISA lab, he was a member of the RNTS CEPICA project and was the main contributor of the IP-Calicot system (Cardiac Arrhythmias Learning for Intelligent Classification of On-line Tracks). Currently, he is involved in the ANR Sweethome project (home automation system using voice command in a smart home), where he is working on decision-making from uncertain and inaccurate sensor data. He has taught courses on Artificial Intelligence in Rennes and in Aberdeen.

Alexander Artikis