Learning to Classify Logical Formulas Based on Their Semantic Similarity

Autor: Ali Ballout, Célia da Costa Pereira, Andrea G. B. Tettamanzi
Přispěvatelé: Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), ANR-21-CE23-0004,CROQUIS,Collecte, représentation, complétion, fusion et interrogation de données de réseaux d'eau urbains hétérogènes et incertaines(2021)
Rok vydání: 2022
Předmět:
Zdroj: PRIMA 2022: Principles and Practice of Multi-Agent Systems ISBN: 9783031212024
Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence
PRIMA 2022-Principles and Practice of Multi-Agent Systems-24th International Conference
PRIMA 2022-Principles and Practice of Multi-Agent Systems-24th International Conference, Nov 2022, Valencia, Spain. pp.364-380, ⟨10.1007/978-3-031-21203-1_22⟩
DOI: 10.1007/978-3-031-21203-1_22
Popis: International audience; An important task in logic, given a formula and a knowledge base which represents what an agent knows of the current state of the world, is to be able to guess the truth value of the formula. Logic reasoners are designed to perform inferences, that is, to decide whether a formula is a logical consequence of the knowledge base, which is stronger than that and can be intractable in some cases. In addition, under the open-world assumption, it may turn out impossible to infer a formula or its negation. In many practical situations, however, when an agent has to make a decision, it is acceptable to resort to heuristic methods to determine the probable veracity or falsehood of a formula, even in the absence of a guarantee of correctness, to avoid blocking the decisionmaking process and move forward. This is why we propose a method to train a classification model based on available knowledge in order to be able of accurately guessing whether an arbitrary, unseen formula is true or false. Our method exploits a kernel representation of logical formulas based on a model-theoretic measure of semantic similarity. The results of experiments show that the proposed method is highly effective and accurate.
Databáze: OpenAIRE
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