3 Search Results for "Katzouris, Nikos"

Document
Short Paper
DOI: 10.4230/LIPIcs.TIME.2025.21
A Translation of Probabilistic Event Calculus into Markov Decision Processes (Short Paper)

Authors: Lyris Xu, Fabio Aurelio D'Asaro, and Luke Dickens

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
Probabilistic Event Calculus (PEC) is a logical framework for reasoning about actions and their effects in uncertain environments, which enables the representation of probabilistic narratives and computation of temporal projections. The PEC formalism offers significant advantages in interpretability and expressiveness for narrative reasoning. However, it lacks mechanisms for goal-directed reasoning. Our work bridges this gap by developing a formal translation of PEC domains into Markov Decision Processes (MDPs), introducing the concept of "action-taking situations" to preserve PEC’s flexible action semantics. The resulting PEC-MDP formalism enables the extensive collection of algorithms and theoretical tools developed for MDPs to be applied to PEC’s interpretable narrative domains. We demonstrate how the translation supports both temporal reasoning tasks and objective-driven planning, with methods for mapping learned policies back into human-readable PEC representations, maintaining interpretability while extending PEC’s capabilities.
Cite as

Lyris Xu, Fabio Aurelio D'Asaro, and Luke Dickens. A Translation of Probabilistic Event Calculus into Markov Decision Processes (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 21:1-21:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{xu_et_al:LIPIcs.TIME.2025.21,
  author =	{Xu, Lyris and D'Asaro, Fabio Aurelio and Dickens, Luke},
  title =	{{A Translation of Probabilistic Event Calculus into Markov Decision Processes}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{21:1--21:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.21},
  URN =		{urn:nbn:de:0030-drops-244674},
  doi =		{10.4230/LIPIcs.TIME.2025.21},
  annote =	{Keywords: Probabilistic Event Calculus, Markov Decision Processes, Temporal Projection, Narrative Reasoning}
}
Document
Extended Abstract
DOI: 10.4230/LIPIcs.TIME.2023.17
Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition (Extended Abstract)

Authors: Nikos Katzouris and Georgios Paliouras

Published in: LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)

Abstract
Complex Event Recognition (CER) systems detect event occurrences in streaming input using predefined event patterns. Techniques that learn event patterns from data are highly desirable in CER. Since such patterns are typically represented by symbolic automata, we propose a family of such automata where the transition-enabling conditions are defined by Answer Set Programming (ASP) rules, and which, thanks to the strong connections of ASP to symbolic learning, are learnable from data. We present such a learning approach in ASP, capable of jointly learning the structure of an automaton and its transition guards' definitions from building-block predicates, and a scalable, incremental version thereof that progressively revises models learnt from mini-batches using Monte Carlo Tree Search. We evaluate our approach on three CER datasets and empirically demonstrate its efficacy.
Cite as

Nikos Katzouris and Georgios Paliouras. Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition (Extended Abstract). In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 17:1-17:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{katzouris_et_al:LIPIcs.TIME.2023.17,
  author =	{Katzouris, Nikos and Paliouras, Georgios},
  title =	{{Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{17:1--17:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.17},
  URN =		{urn:nbn:de:0030-drops-191071},
  doi =		{10.4230/LIPIcs.TIME.2023.17},
  annote =	{Keywords: Event Pattern Learning, Answer Set Programming}
}
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)

Copy BibTex To Clipboard

@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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