User Involvement in Collaborative Decision-Making Dialog Systems

  • Florian Nothdurft
  • Pascal Bercher
  • Gregor Behnke
  • Wolfgang Minker
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 427)

Abstract

Mixed-initiative assistants are systems that support humans in their decision-making and problem-solving capabilities in a collaborative manner. Such systems have to integrate various artificial intelligence capabilities, such as knowledge representation, problem solving and planning, learning, discourse and dialog, and human-computer interaction. These systems aim at solving a given problem autonomously for the user, yet involve the user into the planning process for a collaborative decision-making, to respect e.g. user preferences. However, how the user is involved into the planning can be framed in various ways, using different involvement strategies, varying e.g. in their degree of user freedom. Hence, here we present results of a study examining the effects of different user involvement strategies on the user experience in a mixed-initiative system.

Keywords

Human-computer interaction Cooperative decision-making User experience Dialogue systems 

References

  1. 1.
    Wendemuth, A., Biundo, S.: A companion technology for cognitive technical systems. In: Anna Esposito, Alessandro Vinciarelli, R.H.V.C.M. (ed.) Proceedings of the EUCogII-SSPNET-COST2102 International Conference, pp. 89-103 (2011). Lecture Notes in Computer Science, Springer Berlin Heidelberg (2012)Google Scholar
  2. 2.
    Sohrabi, S., Baier, J., McIlraith, S.A.: HTN planning with preferences. In: Proceedings of the 21st Int. Joint Conference on Artificial Intelligence (IJCAI 2009). pp. 1790-1797. AAAI Press (2009)Google Scholar
  3. 3.
    Nothdurft, F., Behnke, G., Bercher, P., Biundo, S., Minker, W.: The interplay of user-centered dialog systems and ai planning. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). pp. 344-353. Association for Computational Linguistics, Prague, Czech Republic (September 2015)Google Scholar
  4. 4.
    Myers, K.L., Tyson, W.M., Wolverton, M.J., Jarvis, P.A., Lee, T.J., desJardins, M.: PASSAT: a user-centric planning framework. In: Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space, pp. 1-10 (2002)Google Scholar
  5. 5.
    Ai-Chang, M., Bresina, J., Charest, L., Chase, A., Hsu, J.J., Jonsson, A., Kanefsky, B., Morris, P., Rajan, K., Yglesias, J., et al.: Mapgen: mixed-initiative planning and scheduling for the mars exploration rover mission. IEEE Intell. Syst. 19(1), 8–12 (2004)CrossRefGoogle Scholar
  6. 6.
    Fernández-Olivares, J., Castillo, L.A., García-Pérez, Ó., Palao, F.: Bringing users and planning technology together. Experiences in SIADEX. In: Proceedings of the 16th International Conference on Automated Planning and Scheduling (ICAPS 2006). pp. 11-20. AAAI Press (2006)Google Scholar
  7. 7.
    Tecuci, G., Boicu, M., Cox, M.T.: Seven aspects of mixed-initiative reasoning: an introduction to this special issue on mixed-initiative assistants. AI Mag. 28(2), 11 (2007)Google Scholar
  8. 8.
    Behnke, G., Ponomaryov, D., Schiller, M., Bercher, P., Nothdurft, F., Glimm, B., Biundo, S.: Coherence across components in cognitive systems–one ontology to rule them all. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2015). AAAI Press (2015)Google Scholar
  9. 9.
    W3C OWL Working Group: OWL 2 Web Ontology Language: Document Overview (2009). http://www.w3.org/TR/owl2-overview/
  10. 10.
    Honold, F., Schüssel, F., Weber, M.: Adaptive probabilistic fission for multimodal systems. In: Proceedings of the 24th Australian Computer-Human Interaction Conference, pp. 222-231. OzCHI’12, ACM, New York, NY, USA (November, 26-30 2012)Google Scholar
  11. 11.
    Honold, F., Schussel, F., Weber, M., Nothdurft, F., Bertrand, G., Minker, W.: Context models for adaptive dialogs and multimodal interaction. In: Proceedings of the 9th International Conference on Intelligent Environments (IE 2013), pp. 57-64. IEEE (2013)Google Scholar
  12. 12.
    Glodek, M., Honold, F., Geier, T., Krell, G., Nothdurft, F., Reuter, S., Schüssel, F., Hoernle, T., Dietmayer, K., Minker, W., Biundo, S., Weber, M., Palm, G., Schwenker, F.: Fusion paradigms in cognitive technical systems for Human-Computer interaction. Neurocomputing 161, 17–37 (2015)CrossRefGoogle Scholar
  13. 13.
    Schüssel, F., Honold, F., Weber, M.: Using the transferable belief model for multimodal input fusion in companion systems. In: Multimodal Pattern Recognition of Social Signals in HCI, LNCS, vol. 7742, pp. 100-115. Springer (2013)Google Scholar
  14. 14.
    Allen, J.F., Schubert, L.K., Ferguson, G., Heeman, P., Hwang, C.H., Kato, T., Light, M., Martin, N., Miller, B., Poesio, M., et al.: The trains project: a case study in building a conversational planning agent. J. Exp. Theor. Artif. Intell. 7(1), 7–48 (1995)CrossRefMATHGoogle Scholar
  15. 15.
    Ferguson, G., Allen, J.F., et al.: Trips: An integrated intelligent problem-solving assistant. In: Proceedings of the AAAI/IAAI, pp. 567–572 (1998)Google Scholar
  16. 16.
    Rao, A.S., Georgeff, M.P.: Modeling rational agents within a bdi-architecture. KR 91, 473–484 (1991)MathSciNetMATHGoogle Scholar
  17. 17.
    Rich, C., Sidner, C.L.: Collagen: a collaboration manager for software interface agents. User Model. User-Adapt. Interact. 8(3–4), 315–350 (1998)CrossRefGoogle Scholar
  18. 18.
    Grosz, B.J., Kraus, S.: Collaborative plans for complex group action. Artif. Intell. 86(2), 269–357 (1996)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Myers, K.L., Jarvis, P.A., Tyson, W.M., Wolverton, M.J.: A mixed-initiative framework for robust plan sketching. In: Proceedings of the 13th International Conference on Automated Planning and Scheduling (ICAPS 2003). pp. 256-266. AAAI Press (2003)Google Scholar
  20. 20.
    de la Asunción, M., Castillo, L., Fdez-Olivares, J., García-Pérez, Ó., González, A., Palao, F.: Siadex: an interactive knowledge-based planner for decision support in forest fire fighting. AI Commun. 18(4), 257 (2005)MathSciNetGoogle Scholar
  21. 21.
    Rich, C., Sidner, C.L.: Diamondhelp: a generic collaborative task guidance system. AI Mag. 28(2), 33 (2007)Google Scholar
  22. 22.
    Hassenzahl, M., Burmester, M., Koller, F.: Attrakdiff: Ein fragebogen zur messung wahrgenommener hedonischer und pragmatischer qualitt. In: Szwillus, G., Ziegler, J. (eds.) Mensch & Computer 2003: Interaktion in Bewegung, pp. 187–196. B. G. Teubner, Stuttgart (2003)CrossRefGoogle Scholar
  23. 23.
    Kirschner, P.A.: Cognitive load theory: implications of cognitive load theory on the design of learning. Learn. Instr. 12(1), 1–10 (2002)CrossRefGoogle Scholar
  24. 24.
    Paas, F., Van Merriënboer, J.: Variability of worked examples and transfer of geometrical problem-solving skills: a cognitive-load approach. J. Educ. Psychol. 86(1), 122 (1994)CrossRefGoogle Scholar
  25. 25.
    M. Klepsch, F.W., Seufert, T.: Differentiated Measurement of Cognitive Load: Possible or Not? (2015), in preparationGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Florian Nothdurft
    • 1
  • Pascal Bercher
    • 2
  • Gregor Behnke
    • 2
  • Wolfgang Minker
    • 1
  1. 1.Institute of Communications EngineeringUlm UniversityUlmGermany
  2. 2.Institute of Artificial IntelligenceUlm UniversityUlmGermany

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