Advertisement

To Plan for the User Is to Plan with the User: Integrating User Interaction into the Planning Process

  • Gregor Behnke
  • Florian Nielsen
  • Marvin Schiller
  • Denis Ponomaryov
  • Pascal Bercher
  • Birte Glimm
  • Wolfgang Minker
  • Susanne Biundo
Chapter
Part of the Cognitive Technologies book series (COGTECH)

Abstract

Settings where systems and users work together to solve problems collaboratively are among the most challenging applications of Companion-Technology. So far we have seen how planning technology can be exploited to realize Companion-Systems that adapt flexibly to changes in the user’s situation and environment and provide detailed help for users to realize their goals. However, such systems lack the capability to generate their plans in cooperation with the user. In this chapter we go one step further and describe how to involve the user directly into the planning process. This enables users to integrate their wishes and preferences into plans and helps the system to produce individual plans, which in turn let the Companion-System gain acceptance and trust from the user.

Such a Companion-System must be able to manage diverse interactions with a human user. A so-called mixed-initiative planning system integrates several Companion-Technologies which are described in this chapter. For example, a—not yet final—plan, including its flaws and solutions, must be presented to the user to provide a basis for her or his decision. We describe how a dialog manager can be constructed such that it can handle all communication with a user. Naturally, the dialog manager and the planner must use coherent models. We show how an ontology can be exploited to achieve such models. Finally, we show how the causal information included in plans can be used to answer the questions a user might have about a plan.

The given capabilities of a system to integrate user decisions and to explain its own decisions to the user in an appropriate way are essential for systems that interact with human users.

Notes

Acknowledgements

This work was done within the Transregional Collaborative Research Centre SFB/TRR 62 “Companion-Technology for Cognitive Technical Systems” funded by the German Research Foundation (DFG).

References

  1. 1.
    Ai-Chang, M., Bresina, J., Charest, L., Chase, A., Hsu, J.J., Jonsson, A., Kanefsky, B., Morris, P., Rajan, K., Yglesias, J., Chafin, B., Dias, W., Maldague, P.: MAPGEN: mixed-initiative planning and scheduling for the Mars exploration rover mission. IEEE Intell. Syst. 19(1), 8–12 (2004)CrossRefGoogle Scholar
  2. 2.
    Androutsopoulos, I., Lampouras, G., Galanis, D.: Generating natural language descriptions from OWL ontologies: the NaturalOWL system. J. Artif. Intell. Res. 48, 671–715 (2013)MATHGoogle Scholar
  3. 3.
    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 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1442–1449. AAAI Press, Palo Alto, CA (2015)Google Scholar
  4. 4.
    Behnke, G., Höller, D., Bercher, P., Biundo, S.: Change the plan – how hard can that be? In: Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press, Palo Alto, CA (2016)Google Scholar
  5. 5.
    Bercher, P., Höller, D.: Interview with David E. Smith. Künstl. Intell. (2016). doi:10.1007/s13218-015-0403-y. Special Issue on Companion TechnologiesGoogle Scholar
  6. 6.
    Bercher, P., Biundo, S., Geier, T., Hoernle, T., Nothdurft, F., Richter, F., Schattenberg, B.: Plan, repair, execute, explain - how planning helps to assemble your home theater. In: Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS), pp. 386–394. AAAI Press, Palo Alto, CA (2014)Google Scholar
  7. 7.
    Bercher, P., Richter, F., Hörnle, T., Geier, T., Höller, D., Behnke, G., Nothdurft, F., Honold, F., Minker, W., Weber, M., Biundo, S.: A planning-based assistance system for setting up a home theater. In: Proceedings of the 29th National Conference on Artificial Intelligence (AAAI). AAAI Press, Palo Alto, CA (2015)Google Scholar
  8. 8.
    Biundo, S., Schattenberg, B.: From abstract crisis to concrete relief (a preliminary report on combining state abstraction and HTN planning). In: Proceedings of the 6th European Conference on Planning (ECP), pp. 157–168. AAAI Press, Palo Alto, CA (2001)Google Scholar
  9. 9.
    Biundo, S., Höller, D., Schattenberg, B., Bercher, P.: Companion-technology: an overview. Künstl. Intell. (2016). doi:10.1007/s13218-015-0419-3. Special Issue on Companion TechnologiesGoogle Scholar
  10. 10.
    Borgida, A., Franconi, E., Horrocks, I.: Explaining ALC subsumption. In: Proceedings of the 14th European Conference on Artificial Intelligence (ECAI), pp. 209–213. IOS Press, Palo Alto, CA (2000)Google Scholar
  11. 11.
    Byrne, R.: Planning meals: problem solving on a real data-base. Cognition 5, 287–332 (1977)CrossRefGoogle Scholar
  12. 12.
    Erol, K., Hendler, J.A., Nau, D.S.: UMCP: a sound and complete procedure for hierarchical task-network planning. In: Proceedings of the 2nd International Conference on Artificial Intelligence Planning Systems (AIPS), pp. 249–254. AAAI Press, Palo Alto, CA (1994)Google Scholar
  13. 13.
    Ferguson, G., Allen, J.F.: TRIPS: an integrated intelligent problem-solving assistant. In: Proceedings of the 15h National Conference on Artificial Intelligence (AAAI), pp. 567–572. AAAI Press, Palo Alto, CA (1998)Google Scholar
  14. 14.
    Geier, T., Bercher, P.: On the decidability of HTN planning with task insertion. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 1955–1961. AAAI Press, Palo Alto, CA (2011)Google Scholar
  15. 15.
    Gil, Y.: Description logics and planning. AI Mag. 26(2), 73–84 (2005)Google Scholar
  16. 16.
    Hassenzahl, M., Burmester, M., Koller, F.: AttrakDiff: Ein Fragebogen zur Messung wahrgenommener hedonischer und pragmatischer Qualität. In: Mensch & Computer 2003: Interaktion in Bewegung, pp. 187–196. Teubner, Wiesbaden (2003)Google Scholar
  17. 17.
    Hayes-Roth, B., Hayes-Roth, F.: A cognitive model of planning. Cogn. Sci. 3, 275–310 (1979)CrossRefGoogle Scholar
  18. 18.
    Honold, F., Schüssel, F., Weber, M.: Adaptive probabilistic fission for multimodal systems. In: Proceedings of the 24th Australian Computer-Human Interaction Conference (OzCHI), pp. 222–231. ACM, New York (2012)Google Scholar
  19. 19.
    Horridge, M.: Justification based explanations in ontologies. Ph.D. thesis, University of Manchester, Manchester (2011)Google Scholar
  20. 20.
    Horridge, M., Drummond, N., Goodwin, J., Rector, A., Stevens, R., Wang, H.H.: The Manchester OWL syntax. In: Proceedings of the OWLED’06 Workshop on OWL: Experiences and Directions, vol. 216 (2006). CEUR Workshop ProceedingsGoogle Scholar
  21. 21.
    Kuhn, T.: The understandability of OWL statements in controlled English. Semantic Web 4(1), 101–115 (2013)Google Scholar
  22. 22.
    Lin, N., Kuter, U., Sirin, E.: Web service composition with user preferences. In: The Semantic Web: Research and Applications. Lecture Notes in Computer Science, vol. 5021, pp. 629–643. Springer, Berlin (2008)Google Scholar
  23. 23.
    Madsen, M., Gregor, S.: Measuring human-computer trust. In: Proceedings of the 11th Australasian Conference on Information Systems (ACIS), pp. 6–8 (2000)Google Scholar
  24. 24.
    McAllester, D., Rosenblitt, D.: Systematic nonlinear planning. In: Proceedings of the 9th National Conference on Artificial Intelligence (AAAI), pp. 634–639. AAAI Press, Palo Alto, CA (1991)Google Scholar
  25. 25.
    Myers, K.L., Jarvis, P., Tyson, M., Wolverton, M.: A mixed-initiative framework for robust plan sketching. In: 13th International Conference on Automated Planning and Scheduling (ICAPS), pp. 256–266. AAAI Press, Palo Alto, CA (2003)Google Scholar
  26. 26.
    Nau, D.S., Au, T.C., Ilghami, O., Kuter, U., Muñoz-Avila, H., Murdock, J.W., Wu, D., Yaman, F.: Applications of SHOP and SHOP2. IEEE Intell. Syst. 20(2), 34–41 (2005)CrossRefGoogle Scholar
  27. 27.
    NCICB (NCI Center for Bioinformatics): http://ncicb.nci.nih.gov/xml/owl/EVS/ (2015). Thesaurus.owl. Accessed 9 Feb 2015Google Scholar
  28. 28.
    Nguyen, T.A.T., Power, R., Piwek, P., Williams, S.: Predicting the understandability of OWL inferences. In: The Semantic Web: Semantics and Big Data. Lecture Notes in Computer Science, vol. 7882, pp. 109–123. Springer, Berlin (2013)Google Scholar
  29. 29.
    Nothdurft, F., Richter, F., Minker, W.: Probabilistic human-computer trust handling. In: Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 51–59. ACL, Menlo Park, CA (2014). http://www.aclweb.org/anthology/W14-4307Google Scholar
  30. 30.
    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. ACL, Menlo Park, CA (2015)Google Scholar
  31. 31.
    Penberthy, J.S., Weld, D.S.: UCPOP: a sound, complete, partial order planner for ADL. In: Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 103–114. Morgan Kaufmann, Los Altos, CA (1992)Google Scholar
  32. 32.
    Pichler, M., Seufert, T.: Two strategies to measure cognitive load. In: EARLI Conference 2011. Education for a Global Networked Society, pp. 928–929. European Association for Research on Learning and Instruction, Leuven (2011)Google Scholar
  33. 33.
    Pulido, J.C., González, J.C., González-Ferrer, A., García, J., Fernández, F., Bandera, A., Bustos, P., Suárez, C.: Goal-directed generation of exercise sets for upper-limb rehabilitation. In: Proceedings of the 5th Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), pp. 38–45 (2014)Google Scholar
  34. 34.
    Schiller, M., Glimm, B.: Towards explicative inference for OWL. In: Proceedings of the 26th International Description Logic Workshop, vol. 1014, pp. 930–941. CEUR (2013)Google Scholar
  35. 35.
    Schmidt-Schauß, M., Smolka, G.: Attributive concept descriptions with complements. Appl. Artif. Intell. 48, 1–26 (1991)MathSciNetCrossRefMATHGoogle Scholar
  36. 36.
    Sirin, E.: Combining description logic reasoning with AI planning for composition of web services. Ph.D. thesis, University of Maryland at College Park (2006)Google Scholar
  37. 37.
    Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D.: HTN planning for web service composition using SHOP2. Web Semant. 1(4), 377–396 (2004)CrossRefGoogle Scholar
  38. 38.
    Tsarkov, D., Horrocks, I.: FaCT++ description logic reasoner: system description. In: Proceedings of the 3rd International Joint Conference on Automated Reasoning (IJCAR), pp. 292–297. Springer, Berlin (2006)Google Scholar
  39. 39.
    Vardi, M.Y.: Why is modal logic so robustly decidable? Descriptive Complex. Finite Models 31, 149–184 (1997)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gregor Behnke
    • 1
  • Florian Nielsen
    • 2
  • Marvin Schiller
    • 1
  • Denis Ponomaryov
    • 3
  • Pascal Bercher
    • 1
  • Birte Glimm
    • 1
  • Wolfgang Minker
    • 2
  • Susanne Biundo
    • 1
  1. 1.Institute of Artificial IntelligenceUlmGermany
  2. 2.Institute of Communications EngineeringUlmGermany
  3. 3.A.P. Ershov Institute of Informatics SystemsNovosibirskRussia

Personalised recommendations