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Building a Generic Feedback System for Rule-Based Problems

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Abstract

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.

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References

  1. Bylander, T.: The computational complexity of propositional STRIPS planning. Artif. Intell. 69(1–2), 165–204 (1994)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  3. Galagan, N.I.: Problem description language SITPLAN. Cybern. Syst. Anal. 15(2), 255–266 (1979)

    Google Scholar 

  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. Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 81–112 (2007)

    Article  Google Scholar 

  6. Heeren, B., Jeuring, J.: Feedback services for stepwise exercises. Sci. Comput. Program. 88, 110–129 (2014)

    Article  Google Scholar 

  7. Heeren, B., Jeuring, J., Gerdes, A.: Specifying rewrite strategies for interactive exercises. Math. Comput. Sci. 3(3), 349–370 (2010)

    Article  Google Scholar 

  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. 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). https://doi.org/10.1007/978-3-319-24258-3_49

    Chapter  Google Scholar 

  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. Kovacs, D.L.: BNF definition of PDDL 3.1 (2011). http://www.plg.inf.uc3m.es/ipc2011-deterministic/attachments/OtherContributions/kovacs-pddl-3.1-2011.pdf

  12. Kovacs, D.L.: A multi-agent extension of PDDL3. In: WS-IPC 2012, p. 19 (2012)

    Google Scholar 

  13. Lavelle, S.: PuzzleScript (2016). https://github.com/increpare/PuzzleScript

  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. Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education, London (2005)

    Google Scholar 

  16. McDermott, D., et al.: PDDL-The Planning Domain Definition Language (1998)

    Google Scholar 

  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). https://doi.org/10.1007/978-94-017-0819-7_17

    Chapter  Google Scholar 

  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. Plasmeijer, R., van Eekelen, M.: Clean language report version 2.1 (2002)

    Google Scholar 

  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. Russell, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach (3 International Edition). Pearson Education, London (2010)

    MATH  Google Scholar 

  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 2015

    Google Scholar 

  23. Stutterheim, J., Achten, P., Plasmeijer, R.: C2 demo (2016). https://gitlab.science.ru.nl/clean-and-itasks/iTasks-SDK/tree/master/Examples/Applications/c2-demo

  24. VanLehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Educ. 16(3), 227–265 (2006)

    Google Scholar 

  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. 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. 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 

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Acknowledgments

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.

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Correspondence to Nico Naus .

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Naus, N., Jeuring, J. (2019). Building a Generic Feedback System for Rule-Based Problems. In: Van Horn, D., Hughes, J. (eds) Trends in Functional Programming. TFP 2016. Lecture Notes in Computer Science(), vol 10447. Springer, Cham. https://doi.org/10.1007/978-3-030-14805-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-14805-8_10

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