AFFLOG: A Logic Based Affective Tutoring System

  • Achilles Dougalis
  • Dimitris PlexousakisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12149)


In this work, the Affective Logic (AFFLOG) Tutor is presented. An Affective Tutoring System that uses knowledge representation and reasoning tools such as Answer Set Programming and the Event Calculus (EC) in order to represent the main components of the tutor. AI Planning is used to select individual parts of a given course material (tutorials) in order to build a specific course tailored to the needs of each user according to the user’s learning preferences. This course can dynamically change during the teaching session responding to the user’s mental and emotional states, providing affective support by offering praise, consolation or encouragement depending on the current emotion of the user. The design and a functioning implementation of the system is presented. As a proof of concept, a course on how to play the Settlers of Catan(c) board game was designed and implemented.


Affective Tutoring Systems Adaptive Learning Systems Answer Set Programming Event Calculus 


  1. 1.
    Anderson, J.R., Boyle, C.F., Corbett, A.T., Lewis, M.W.: Cognitive modeling and intelligent tutoring. Artif. Intell. 42(1), 7–49 (1990)CrossRefGoogle Scholar
  2. 2.
    Brewka, G., Eiter, T., Truszczyński, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  3. 3.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper & Row, New York (1990)Google Scholar
  4. 4.
    Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. J. Educ. Media 29(3), 241–250 (2004)CrossRefGoogle Scholar
  5. 5.
    D’mello, S., Graesser, A.: AutoTutor and affective AutoTutor: learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Trans. Interact. Intell. Syst. (TiiS). 2(4), 23 (2012)Google Scholar
  6. 6.
    Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3-4), 169–200 (1992)CrossRefGoogle Scholar
  7. 7.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)Google Scholar
  8. 8.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, R., Schaub, T., Schneider, M.: Potassco: the Potsdam answer set solving collection. Ai Commun. 24(2), 107–124 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. ICLP/SLP 88, 1070–1080 (1988)Google Scholar
  10. 10.
    Google Vision API, Google Cloud Platform. Accessed 12 Feb 2020
  11. 11.
    Honey, P., Mumford, A.: The Manual of Learning Styles. P. Honey, London (1992)Google Scholar
  12. 12.
    Marek, V.W., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: Apt, K.R., Marek, V.W., Truszczynski, M., Warren, D.S. (eds.) The Logic Programming Paradigm. AI, pp. 375–398. Springer, Heidelberg (1999). Scholar
  13. 13.
    Miller, R., Shanahan, M.: Some alternative formulations of the event calculus. In: Kakas, Antonis C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 452–490. Springer, Heidelberg (2002). Scholar
  14. 14.
    O’Rourke, E., Butler, E., Díaz Tolentino, A., Popović, Z.: Automatic generation of problems and explanations for an intelligent algebra tutor. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 383–395. Springer, Cham (2019). Scholar
  15. 15.
    Pandit, D., Bansal, A.: A declarative approach for an adaptive framework for learning in online courses. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol. 1. IEEE (2019)Google Scholar
  16. 16.
    Ragusa, C., Hoffman, M., Leonard, J.: 10 unwrapping GIFT A primer on developing with the generalized intelligent framework for tutoring (2013)Google Scholar
  17. 17.
    Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)CrossRefGoogle Scholar
  18. 18.
    Sergot, M., Kowalski, R.: A logic-based calculus of events. New Gener. Comput. 4(1), 67–95 (1986)CrossRefGoogle Scholar
  19. 19.
    Tenorth, M., Beetz, M.: Representations for robot knowledge in the KnowRob framework. Artif. Intell. 247, 151–169 (2017)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. Learn. Technol. 4(3–4), 129–164 (2009)CrossRefGoogle Scholar
  21. 21.
    Yang, T.C., Hwang, G.J., Yang, S.J.H.: Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. J. Educ. Technol. Soc. 16, 185–200 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of CreteHeraklionGreece
  2. 2.Institute of Computer ScienceFoundation for Research and Technology HellasHeraklionGreece

Personalised recommendations