Incremental learning of event definitions with Inductive Logic Programming

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• 作者：Nikos Katzouris ; Alexander Artikis ; Georgios Paliouras
• 关键词：Incremental learning ; Abductive–Inductive Logic Programming ; Event Calculus ; Event recognition
• 刊名：Machine Learning
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：100
• 期：2-3
• 页码：555-585
• 全文大小：810 KB
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• 作者单位：Nikos Katzouris (1) (2)
  Alexander Artikis (1) (3)
  Georgios Paliouras (1)
  
  1. Institute of Informatics and Telecommunications, National Center for Scientific Research “Demokritos- Athens, Greece
  2. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
  3. Department of Informatics, University of Piraeus, Piraeus, Greece
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Computing Methodologies
  Simulation and Modeling
  Language Translation and Linguistics
  
• 出版者：Springer Netherlands
• ISSN：1573-0565

文摘

Event recognition systems rely on knowledge bases of event definitions to infer occurrences of events in time. Using a logical framework for representing and reasoning about events offers direct connections to machine learning, via Inductive Logic Programming (ILP), thus allowing to avoid the tedious and error-prone task of manual knowledge construction. However, learning temporal logical formalisms, which are typically utilized by logic-based event recognition systems is a challenging task, which most ILP systems cannot fully undertake. In addition, event-based data is usually massive and collected at different times and under various circumstances. Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence. In this work we present an incremental method for learning and revising event-based knowledge, in the form of Event Calculus programs. The proposed algorithm relies on abductive–inductive learning and comprises a scalable clause refinement methodology, based on a compressive summarization of clause coverage in a stream of examples. We present an empirical evaluation of our approach on real and synthetic data from activity recognition and city transport applications.