Building a Generic Feedback System for Rule-Based Problems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10447)


We present a generic framework that provides hints on how to achieve a goal to users of software supporting rule-based problem solving from different domains. Our approach consists of two parts. First, we present a DSL that relates and unifies different rule-based problems. Second, we use generic search algorithms to solve various kinds of problems. This solution can then be used to calculate a hint for the user. We present three rule-based problem frameworks to illustrate our approach: the Ideas framework, PuzzleScript and iTasks. By taking real world examples from these three example frameworks and instantiating feedback systems for them, we validate our approach.



This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organization for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs.


  1. 1.
    Bylander, T.: The computational complexity of propositional STRIPS planning. Artif. Intell. 69(1–2), 165–204 (1994)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Fikes, R., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3–4), 189–208 (1971)CrossRefGoogle Scholar
  3. 3.
    Galagan, N.I.: Problem description language SITPLAN. Cybern. Syst. Anal. 15(2), 255–266 (1979)Google Scholar
  4. 4.
    Gerdes, A., Jeuring, J., Heeren, B.: An interactive functional programming tutor. In: Lapidot, T., Gal-Ezer, J., Caspersen, M.E., Hazzan, O. (eds) Proceedings of ITICSE 2012: The 17th Annual Conference on Innovation and Technology in Computer Science Education, pp. 250–255. ACM (2012)Google Scholar
  5. 5.
    Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 81–112 (2007)CrossRefGoogle Scholar
  6. 6.
    Heeren, B., Jeuring, J.: Feedback services for stepwise exercises. Sci. Comput. Program. 88, 110–129 (2014)CrossRefGoogle Scholar
  7. 7.
    Heeren, B., Jeuring, J., Gerdes, A.: Specifying rewrite strategies for interactive exercises. Math. Comput. Sci. 3(3), 349–370 (2010)CrossRefGoogle Scholar
  8. 8.
    Hewitt, C.: PLANNER: a language for proving theorems in robots. In: Proceedings of the 1st International Joint Conference on Artificial Intelligence, Washington, DC, May 1969, pp. 295–302 (1969)Google Scholar
  9. 9.
    Jeuring, J., et al.: Communicate!—A serious game for communication skills. In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, É. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 513–517. Springer, Cham (2015). Scholar
  10. 10.
    Junghanns, A., Schaeffer, J., Sokoban: a challenging single-agent search problem. In: IJCAI Workshop on Using Games as an Experimental Testbed for AI Reasearch (1997)Google Scholar
  11. 11.
  12. 12.
    Kovacs, D.L.: A multi-agent extension of PDDL3. In: WS-IPC 2012, p. 19 (2012)Google Scholar
  13. 13.
    Lavelle, S.: PuzzleScript (2016).
  14. 14.
    Lim, C.-U., Fox Harrell, D.: An approach to general videogame evaluation and automatic generation using a description language. In: Proceedings of IEEE CIG 2014: Conference on Computational Intelligence and Games, pp. 1–8 (2014)Google Scholar
  15. 15.
    Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education, London (2005)Google Scholar
  16. 16.
    McDermott, D., et al.: PDDL-The Planning Domain Definition Language (1998)Google Scholar
  17. 17.
    Murray, T.: An overview of intelligent tutoring system authoring tools: updated analysis of the state of the art. In: Murray, T., Blessing, S.B., Ainsworth, S. (eds.) Authoring Tools for Advanced Technology Learning Environments, pp. 491–544. Springer, Dordrecht (2003). Scholar
  18. 18.
    Plasmeijer, R., Lijnse, B., Michels, S., Achten, P., Koopman, P.W.M.: Task-oriented programming in a pure functional language. In: Proceedings of PPDP 2012: Principles and Practice of Declarative Programming, pp. 195–206. ACM (2012)Google Scholar
  19. 19.
    Plasmeijer, R., van Eekelen, M.: Clean language report version 2.1 (2002)Google Scholar
  20. 20.
    Reinefeld, A.: Complete solution of the eight-puzzle and the benefit of node ordering in IDA. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambéry, France, 28 August–3 September 1993, pp. 248–253 (1993)Google Scholar
  21. 21.
    Russell, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach (3 International Edition). Pearson Education, London (2010)zbMATHGoogle Scholar
  22. 22.
    Stutterheim, J., Achten, P., Plasmeijer, R.: Static and dynamic visualisations of monadic programs. In: Implementation and Application of Functional Languages, Koblenz, Germany, pp. 1–13, December 2015Google Scholar
  23. 23.
  24. 24.
    VanLehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Educ. 16(3), 227–265 (2006)Google Scholar
  25. 25.
    VanLehn, K., et al.: The Andes physics tutoring system: lessons learned. Int. J. Artif. Intell. Educ. 15(3), 147–204 (2005)Google Scholar
  26. 26.
    Visser, E., Benaissa, Z.-E.-A., Tolmach, A.P.: Building program optimizers with rewriting strategies. In: Proceedings of the Third ACM SIGPLAN International Conference on Functional Programming (ICFP 1998), Baltimore, Maryland, USA, 27–29 September 1998, pp. 13–26 (1998)Google Scholar
  27. 27.
    Younes, H.L.S., Littman, M.L.: PPDDL1. 0: the language for the probabilistic part of IPC-4. In: Proceedings of the International Planning Competition (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Utrecht UniversityUtrechtThe Netherlands
  2. 2.Faculty of Management, Science and TechnologyOpen University of the NetherlandsHeerlenThe Netherlands

Personalised recommendations