3 Search Results for "Paliouras, George"

Document
DOI: 10.4230/LIPIcs.TIME.2024.5
Open the Chests: An Environment for Activity Recognition and Sequential Decision Problems Using Temporal Logic

Authors: Ivelina Stoyanova, Nicolas Museux, Sao Mai Nguyen, and David Filliat

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

Abstract
This article presents Open the Chests, a novel benchmark environment designed for simulating and testing activity recognition and reactive decision-making algorithms. By leveraging temporal logic, Open the Chests offers a dynamic, event-driven simulation platform that illustrates the complexities of real-world systems. The environment contains multiple chests, each representing an activity pattern that an interacting agent must identify and respond to by pressing a corresponding button. The agent must analyze sequences of asynchronous events generated by the environment to recognize these patterns and make informed decisions. With the aim of theoretically grounding the environment, the Activity-Based Markov Decision Process (AB-MDP) is defined, allowing to model the context-dependent interaction with activities. Our goal is to propose a robust tool for the development, testing, and bench-marking of algorithms that is illustrative of realistic scenarios and allows for the isolation of specific complexities in event-driven environments.
Cite as

Ivelina Stoyanova, Nicolas Museux, Sao Mai Nguyen, and David Filliat. Open the Chests: An Environment for Activity Recognition and Sequential Decision Problems Using Temporal Logic. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 5:1-5:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{stoyanova_et_al:LIPIcs.TIME.2024.5,
  author =	{Stoyanova, Ivelina and Museux, Nicolas and Nguyen, Sao Mai and Filliat, David},
  title =	{{Open the Chests: An Environment for Activity Recognition and Sequential Decision Problems Using Temporal Logic}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{5:1--5:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.5},
  URN =		{urn:nbn:de:0030-drops-212128},
  doi =		{10.4230/LIPIcs.TIME.2024.5},
  annote =	{Keywords: Event-Based Decision Making, Activity Recognition, Temporal Logic, Reinforcement Learning, Dynamic Systems, Complex Event Processing, Benchmark Environment, Real-Time Simulation}
}
Document
Position
DOI: 10.4230/TGDK.2.1.2
Grounding Stream Reasoning Research

Authors: Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract
In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
Cite as

Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer. Grounding Stream Reasoning Research. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 2:1-2:47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{bonte_et_al:TGDK.2.1.2,
  author =	{Bonte, Pieter and Calbimonte, Jean-Paul and de Leng, Daniel and Dell'Aglio, Daniele and Della Valle, Emanuele and Eiter, Thomas and Giannini, Federico and Heintz, Fredrik and Schekotihin, Konstantin and Le-Phuoc, Danh and Mileo, Alessandra and Schneider, Patrik and Tommasini, Riccardo and Urbani, Jacopo and Ziffer, Giacomo},
  title =	{{Grounding Stream Reasoning Research}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:47},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.2},
  URN =		{urn:nbn:de:0030-drops-198597},
  doi =		{10.4230/TGDK.2.1.2},
  annote =	{Keywords: Stream Reasoning, Stream Processing, RDF streams, Streaming Linked Data, Continuous query processing, Temporal Logics, High-performance computing, Databases}
}
Document
DOI: 10.4230/LIPIcs.TIME.2018.4
Predicting the Evolution of Communities with Online Inductive Logic Programming

Authors: George Athanasopoulos, George Paliouras, Dimitrios Vogiatzis, Grigorios Tzortzis, and Nikos Katzouris

Published in: LIPIcs, Volume 120, 25th International Symposium on Temporal Representation and Reasoning (TIME 2018)

Abstract
In the recent years research on dynamic social network has increased, which is also due to the availability of data sets from streaming media. Modeling a network's dynamic behaviour can be performed at the level of communities, which represent their mesoscale structure. Communities arise as a result of user to user interaction. In the current work we aim to predict the evolution of communities, i.e. to predict their future form. While this problem has been studied in the past as a supervised learning problem with a variety of classifiers, the problem is that the "knowledge" of a classifier is opaque and consequently incomprehensible to a human. Thus we have employed first order logic, and in particular the event calculus to represent the communities and their evolution. We addressed the problem of predicting the evolution as an online Inductive Logic Programming problem (ILP), where the issue is to learn first order logical clauses that associate evolutionary events, and particular Growth, Shrinkage, Continuation and Dissolution to lower level events. The lower level events are features that represent the structural and temporal characteristics of communities. Experiments have been performed on a real life data set form the Mathematics StackExchange forum, with the OLED framework for ILP. In doing so we have produced clauses that model both short term and long term correlations.
Cite as

George Athanasopoulos, George Paliouras, Dimitrios Vogiatzis, Grigorios Tzortzis, and Nikos Katzouris. Predicting the Evolution of Communities with Online Inductive Logic Programming. In 25th International Symposium on Temporal Representation and Reasoning (TIME 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 120, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{athanasopoulos_et_al:LIPIcs.TIME.2018.4,
  author =	{Athanasopoulos, George and Paliouras, George and Vogiatzis, Dimitrios and Tzortzis, Grigorios and Katzouris, Nikos},
  title =	{{Predicting the Evolution of Communities with Online Inductive Logic Programming}},
  booktitle =	{25th International Symposium on Temporal Representation and Reasoning (TIME 2018)},
  pages =	{4:1--4:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-089-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{120},
  editor =	{Alechina, Natasha and N{\o}rv\r{a}g, Kjetil and Penczek, Wojciech},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2018.4},
  URN =		{urn:nbn:de:0030-drops-97691},
  doi =		{10.4230/LIPIcs.TIME.2018.4},
  annote =	{Keywords: Social Network Analysis, Community Evolution Prediction, Machine Learning, Inductive Logic Programming, Event Calculus, Online Learning}
}
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