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Inference Engines Performance in Reasoning Tasks for Intelligent Tutoring Systems

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12950))

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Abstract

The use of formal knowledge representation models in intelligent tutoring systems often requires logical reasoning on these models by predefined rules. This process can be time and memory consuming, so finding effective software reasoners for different applications is an important research field. This problem is relevant for cognitive and constraint-based intelligent tutoring systems. We performed a comparative study of various software reasoners (Pellet, Apache Jena inference subsystem, Apache Jena SPARQL query processor, SWI-Prolog with semweb package, Closed World Machine, and Answer Set Programming solvers Clingo and DLV) for solving tasks specific to intelligent tutoring systems using three formal models with different properties and corresponding rule sets created for intelligent tutoring systems in introductory programming education domain. We compared features of rule-definition formalisms for different approaches and measured run and wall time, average CPU load, and peak RAM usage based on the count of inferred RDF triples. The experiments show that Apache Jena infers the solution quicker than other reasoners on the majority of tasks but consumes a significant amount of memory, while Clingo performs significantly better for combinatorial problems.

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Acknowledgment

The reported study was funded by RFBR, project number 20-07-00764 “Conceptual modeling of the knowledge domain on the comprehension level for intelligent decision-making systems in the learning”.

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Authors and Affiliations

  1. Volgograd State Technical University, Lenin Ave, 28, Volgograd, 400005, Russia

    Oleg A. Sychev, Anton Anikin & Mikhail Denisov

Authors
  1. Oleg A. Sychev
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  2. Anton Anikin
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  3. Mikhail Denisov
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Corresponding author

Correspondence to Anton Anikin .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Sychev, O.A., Anikin, A., Denisov, M. (2021). Inference Engines Performance in Reasoning Tasks for Intelligent Tutoring Systems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-86960-1_33

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  • Online ISBN: 978-3-030-86960-1

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