Promoting Independence with a Schedule Management Assistant that Anticipates Disruptions


We motivate and overview a system for schedule management assistance that we are developing specifically to help adolescents with disabilities who are transitioning to independent adulthood. We summarize how we have overcome a number of engineering challenges in creating a prototype system. The expert feedback on our prototype suggests how and why the tool is expected to be useful, and has directed our focus toward handling schedule disruptions. In the latter part of this paper, we provide deeper technical material on new metrics and strategies for giving scheduling advice that is resilient to disruptions while also giving the user more freedom.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13


  1. 1.

    If on a particular day the activity’s time needs fall outside this range, or it needs to start sooner or end later than usual, then as will be explained shortly the user can “Modify” the activity in the midst of the day.

  2. 2.

    Improving this part of the interface to strike the right balance between informing and not overwhelming the user is an area of future research (Section 13).

  3. 3.

    In our prototype, we “fast forward” to the end of the chosen activity in simulation by tapping the UI area labeled “Advance system time to next decision point.”

  4. 4.

    In our user interface (Section 6), this is the upper bound on the “other” activity’s time.


  1. 1.

    AbleLink: Endeavor 3.

  2. 2.

    Barbulescu L, Rubinstein ZB, Smith SF, Zimmerman T (2010) Distributed coordination of mobile agent teams: the advantage of planning ahead. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems, pp 1331–1338

  3. 3.

    Boerkoel J Jr, Durfee EH (2013) Distributed reasoning for multiagent simple temporal problems. J Artif Intell Resh (JAIR) 47:95–156

    MathSciNet  Article  Google Scholar 

  4. 4.

    Boerkoel JC Jr (2012) Distributed approaches for solving constraint-based multiagent scheduling problems. University of Michigan, Ph.D. thesis

    Google Scholar 

  5. 5.

    Boerkoel JC Jr, Durfee EH (2012) A distributed approach to summarizing spaces of multiagent schedules. In: Association for the advancement of artificial intelligence, pp 1742–1748

  6. 6.

    Boerkoel J Jr, Durfee EH (2013) Decoupling the multiagent disjunctive temporal problem. In: Proceedings of the 2013 international conference on autonomous agents and multi-agent systems, pp 1145–1146

  7. 7.

    Boerkoel J Jr, Planken L, Wilcox R, Shah JA (2013) Distributed algorithms for incrementally maintaining multiagent simple temporal networks. In: International conference on automated planning and scheduling, pp 11–19

  8. 8.

    Brooks J, Reed E, Gruver A, Boerkoel JC (2015) Robustness in probabilistic temporal planning. In: AAAI, pp 3239–3246

  9. 9.

    Capterra: scheduling software buyers guide.

  10. 10.

    Conrad PR, Williams BC (2011) Drake: an efficient executive for temporal plans with choice. J Artif Intell Res, 607–659

  11. 11.


  12. 12.

    Dechter R, Meiri I, Pearl J (1991) Temporal constraint networks. Artif Intell 49(1):61–95

    MathSciNet  Article  Google Scholar 

  13. 13.

    Durfee EH, Boerkoel JC, Sleight J (2014) Using hybrid scheduling for the semi-autonomous formation of expert teams. Futur Gener Comput Syst 31:200–212

    Article  Google Scholar 

  14. 14.


  15. 15.

    Gomes CP (2001) On the intersection of AI and OR. Knowl Eng Rev 16 (01):1–4

    Article  Google Scholar 

  16. 16.

    Goodin RE, Rice JM, Parpo A, Eriksson L (2008) Discretionary time: a new measure of freedom. Cambridge University Press

  17. 17.

    Google: Calendar.

  18. 18.

    Hunsberger L (2002) Algorithms for a temporal decoupling problem in multi-agent planning. AAAI/IAAI 2002:468–475

    Google Scholar 

  19. 19.

    Hunsberger L (2003) Distributing the control of a temporal network among multiple agents. In: Proceedings of the second international joint conference on autonomous agents and multiagent systems, pp 899–906

  20. 20.

    Hunsberger L (2009) Fixing the semantics for dynamic controllability and providing a more practical characterization of dynamic execution strategies. In: 16th International symposium on temporal representation and reasoning (TIME 2009). IEEE, pp 155–162

  21. 21.

    Laborie P (2003) Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results. Artif Intell 143(2):151–188

    MathSciNet  Article  Google Scholar 

  22. 22.

    Lau HC, Li J, Yap RH (2006) Robust controllability of temporal constraint networks under uncertainty. In: 18th IEEE International conference on tools with artificial intelligence (ICTAI’06). IEEE, pp 288–296

  23. 23.

    Lennon J, Klages K, Amaro C, Murray C, Holmbeck G (2015) Longitudinal study of neuropsychological functioning and internalizing symptoms in youth with spina bifida: social competence as a mediator. Pediatric Psychol 40(3):336–348

    Article  Google Scholar 

  24. 24.

    Microsoft: Outlook.

  25. 25.

    Morris PH, Muscettola N (2005) Temporal dynamic controllability revisited. In: AAAI, pp 1193– 1198

  26. 26.

    Nelson B, Kumar TS (2008) Circuittsat: a solver for large instances of the disjunctive temporal problem. In: ICAPS, pp 232–239

  27. 27.

    Oddi A, Rasconi R, Cesta A (2010) Casting project scheduling with time windows as a DTP. In: Proceedings of the ICAPS workshop on constraint satisfaction techniques for planning and scheduling problems (COPLAS 2010), pp 42–49

  28. 28.

    Peintner B, Venable KB, Yorke-Smith N (2007) Strong controllability of disjunctive temporal problems with uncertainty. In: Principles and practice of constraint programming–CP 2007. Springer, pp 856–863

  29. 29.

    Planken L, De Weerdt M, Van der Krogt R (2008) P3C: a new algorithm for the simple temporal problem. In: Proceedings of the eighteenth international conference on automated planning and scheduling (ICAPS 2008), pp 256–263

  30. 30.

    Planken L, de Weerdt M, Yorke-Smith N (2010) Incrementally solving STNs by enforcing partial path consistency. In: ICAPS, pp 129–136

  31. 31.

    Shah JA, Stedl J, Williams BC, Robertson P (2007) A fast incremental algorithm for maintaining dispatchability of partially controllable plans. In: ICAPS, pp 296–303

  32. 32.

    Shah JA, Williams BC (2008) Fast dynamic scheduling of disjunctive temporal constraint networks through incremental compilation. In: ICAPS, pp 322–329

  33. 33.


  34. 34.

    Smith SF, Gallagher A, Zimmerman T (2007) Distributed management of flexible time schedules. In: Proceedings of the 6th international joint conference on autonomous agents and multiagent systems (AAMAS07), pp 472–479

  35. 35.

    Tarazi R, Zabel T, Mahone E (2008) Age-related differences in executive function among children with spina bifida/hydrocephalus based on parent behavior ratings. Clin Neuropsychol 22(4):585– 602

    Article  Google Scholar 

  36. 36.


  37. 37.

    Tsamardinos I (2001) Constraint-based temporal reasoning algorithms with applications to planning. University of Pittsburgh, Ph.D. thesis

    Google Scholar 

  38. 38.

    Tsamardinos I, Pollack ME (2003) Efficient solution techniques for disjunctive temporal reasoning problems. Artif Intell 151(1):43–89

    MathSciNet  Article  Google Scholar 

  39. 39.

    Tsamardinos I, Vidal T, Pollack ME (2003) CTP: a new constraint-based formalism for conditional, temporal planning. Constraints 8(4):365–388

    MathSciNet  Article  Google Scholar 

  40. 40.

    Venable KB, Yorke-Smith N (2005) Disjunctive temporal planning with uncertainty. In: International joint conference on artificial intelligence, pp 1721–1722

  41. 41.

    Vidal T (1999) Handling contingency in temporal constraint networks: from consistency to controllabilities. J Exper Theor Artif Intell 11(1):23–45

    MathSciNet  Article  Google Scholar 

  42. 42.

    Vidal T, Fargier H (1999) Handling contingency in temporal constraint networks: from consistency to controllabilities. J Exper Theor Artif Intell 11:23–45

    Article  Google Scholar 

  43. 43.

    Warschausky S, Kaufman JN, Evitts M, Schutt W, Hurvitz EA (2017) Mastery motivation and executive functions as predictors of adaptive behavior in adolescents and young adults with cerebral palsy or myelomeningocele. Rehabil Psychol 62(3):258–267

    Article  Google Scholar 

  44. 44.

    Warschausky S, Kaufman JN, Schutt W, Evitts M, Hurvitz EA (2017) Health self-management, transition readiness and adaptive behavior in persons with cerebral palsy or myelomeningocele. Rehabil Psychol 62(3):268–275

    Article  Google Scholar 

  45. 45.

    Wikipedia: Comparison of xml editors.

  46. 46.

    Wilson M, Klos T, Witteveen C, Huisman B (2013) Flexibility and decoupling in the simple temporal problem. In: BNAIC 2013: proceedings of the 25th benelux conference on artificial intelligence, Delft

  47. 47.

    Xu L, Choueiry BY (2003) A new efficient algorithm for solving the simple temporal problem. In: Proceedings of the tenth international symposium on temporal representation and reasoning, and fourth international conference on temporal logic (TIME-ICTL 03), pp 212–222

  48. 48.

    Zabel T, Jacobson LA, Zachik C, Levey E, Kinsman S, Mahone E (2011) Parent- and self-ratings of executive functions in adolescents and young adults with spina bifida. Clin Neuropsychol 25(6):926–941

    Article  Google Scholar 

Download references


Thanks to our collaborators (Dr. Ned Kirsch, Dr. Jason Sleight, Donna Omichinski, Drew Davis, Jordan McKay, and Drew Canada).


This work has been supported, in part, by the by the US HHS under NIDILRR grant 90RE5012.

Author information



Corresponding author

Correspondence to Edmund H. Durfee.

Ethics declarations

The opinions and views expressed in this document do not necessarily reflect those of NIDILRR.

Conflict of interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Durfee, E.H., Garrett, L.H. & Johnson, A. Promoting Independence with a Schedule Management Assistant that Anticipates Disruptions. J Healthc Inform Res 4, 19–49 (2020).

Download citation


  • Schedule management
  • Assistive technology
  • Temporal reasoning