Semantically Enhanced Virtual Learning Environments Using Sunflower

  • Daniel Elenius
  • Grit DenkerEmail author
  • Minyoung Kim
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 672)


Teaching procedural skills is relevant for a broad range of applications, from IT administration to automotive repair to medical diagnostics. Virtual learning environments reduce the cost, time, and risk, and increase the availability of such training. We introduce ontologies and rules to characterize the objects in the learning environment, and the actions that the user can perform on them. These semantic models are used as the basis for automated reasoning about a student’s actions and their effects, and guide automated assessment and feedback to the student. We describe our system and models in the context of weapon skills such as disassembling and assembling a rifle.


Virtual Environment Semantic Model Action Rule Procedural Skill Ontological Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This material is based upon work supported by the United States Government under Contract No. W911QY-14-C-0023. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Government. Development of the Sunflower IDE was funded in part by the U.S. Office of the Assistant Secretary of Defense for Readiness under the Open Netcentric Interoperability for Training and Testing (ONISTT) project, and by TRMC (Test Resource Management Center) T&E/S&T (Test and Evaluation/Science and Technology) Program under the NST Test Technology Area. We are also indebted to the research community for developing and maintaining the open source language and software components on which Sunflower depends, especially Flora-2 (a.k.a. Ergo Lite), XSB Prolog, and InterProlog.


  1. 1.
    Soldier’s manual of common tasks - warrior skills level 1. Technical report, Headquarters Department of the Army, September 2012Google Scholar
  2. 2.
    Ford, R., Denker, G., Elenius, D., Moore, W., Abi-Lahoud, E.: Automating financial regulatory compliance using ontology+rules and Sunflower. In: Proceedings of SEMANTICS (2016). (to appear)Google Scholar
  3. 3.
    Greuel, C., Myers, K.: Assessment and content authoring in semantically enabled virtual environments. In: Proceedings of Interservice/Industry Training, Simulation and Education Conference (2016). (submitted)Google Scholar
  4. 4.
    Kessing, J., Tutenel, T., Bidarra, R.: Designing semantic game worlds. In: Proceedings of the The Third Workshop on Procedural Content Generation in Games, PCG 2012, ACM, New York, NY, USA (2012).
  5. 5.
    Maderer, J., Gütl, C., AL-Smadi, M.: Formative assessment in immersive environments: a semantic approach to automated evaluation of user behavior in open wonderland. In: Proceedings of Immersive Education (iED) Summit, June 2013Google Scholar
  6. 6.
    Myers, K., Gervasio, M.: Solution authoring via demonstration and annotation: an empirical study. In: Proceedings of International Conference on Advanced Learning Technologies (2016). (submitted)Google Scholar
  7. 7.
    Nau, D., Ghallab, M., Traverso, P.: Automated Planning: Theory & Practice. Morgan Kaufmann Publishers Inc., San Francisco (2004)Google Scholar
  8. 8.
    Riehemann, S., Elenius, D.: Ontological analysis of terrain data. In: Liao, L. (ed.) ACM International Conference Proceeding Series on COM.Geo, p. 10. ACM (2011)Google Scholar
  9. 9.
    Vujosevic, R., Ianni, J.: A taxonomy of motion models for simulation and analysis of maintenance tasks. Technical report, United States Air Force Armstrong Laboratory, January 1997Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.SRI InternationalMenlo ParkUSA

Personalised recommendations