Learning to Detect Complex Events with Expert Advice

Systems for symbolic event recognition detect occurrences of events in streaming input using a set of event patterns in the form of temporal logical rules. Algorithms for online learning/revising such patterns should be capable of updating the current event pattern set without compromising the quality of the provided service, i.e. the system’s online predictive performance. Towards this, we present an approach based on Prediction with Expert Advice. The experts in our approach are logical rules representing event patterns, which are learnt online via a single-pass strategy. To handle the dynamic nature of the task, an Event Calculus-inspired prediction/event detection scheme allows to incorporate commonsense principles into the learning process.We present a preliminary empirical assessment with promising results

A Probabilistic Logic Programming Event Calculus

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.Comment: Accepted for publication in the Theory and Practice of Logic Programming (TPLP) journa

An ontological analysis of vague motion verbs, with an application to event recognition

This research presents a methodology for the ontological formalisation of vague spatial concepts from natural language, with an application to the automatic recognition of event occurrences on video data. The main issue faced when defining concepts sourced from language is vagueness, related to the presence of ambiguities and borderline cases even in simple concepts such as ‘near’, ‘fast’, ‘big’, etc. Other issues specific to this semantic domain are saliency, granularity and uncertainty. In this work, the issue of vagueness in formal semantics is discussed and a methodology based on supervaluation semantics is proposed. This constitutes the basis for the formalisation of an ontology of vague spatial concepts based on classical logic, Event Calculus and supervaluation semantics. This ontology is structured in layers where high-level concepts, corresponding to complex actions and events, are inferred through mid-level concepts, corresponding to simple processes and properties of objects, and low-level primitive concepts, representing the most essential spatio-temporal characteristics of the real world. The development of ProVision, an event recognition system based on a logic-programming implementation of the ontology, demonstrates a practical application of the methodology. ProVision grounds the ontology on data representing the content of simple video scenes, leading to the inference of event occurrences and other high-level concepts. The contribution of this research is a methodology for the semantic characterisation of vague and qualitative concepts. This methodology addresses the issue of vagueness in ontologies and demonstrates the applicability of a supervaluationist approach to the formalisation of vague concepts. It is also proven to be effective towards solving a practical reasoning task, such as the event recognition on which this work focuses

Indexing the Event Calculus with Kd-trees to Monitor Diabetes

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. A patient affected by a chronic disease can generate large amounts of events. Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. This is a large number of events to monitor for medical doctors, in particular when considering that they may have to take decisions concerning adjusting the treatment, which may impact the life of the patients for a long time. Given the need to analyse such a large stream of data, doctors need a simple approach towards physiological time series that allows them to promptly transfer their knowledge into queries to identify interesting patterns in the data. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. In order to tackle the knowledge representation and efficiency problem, this contribution presents the kd-tree cached event calculus (\ceckd) an event calculus extension for knowledge engineering of temporal rules capable to handle many thousands events produced by a diabetic patient. \ceckd\ is built as a support to a graphical interface to represent monitoring rules for diabetes type 1. In addition, the paper evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show how indexing events using kd-trees improves scalability with respect to the current state of the art.Comment: 24 pages, preliminary results calculated on an implementation of CECKD, precursor to Journal paper being submitted in 2017, with further indexing and results possibilities, put here for reference and chronological purposes to remember how the idea evolve