Nowadays, smart homes can be conveniently programmed by end-users. One of the most
popular ways is using trigger-action programming (TAP), which is based on rules in the
simple form of “IF a trigger occurs, THEN performs an action.” Programming smart homes
with TAP is generally considered to be easy to learn and very approachable by end-users.
However, this simplicity does not always fulfill the users’ needs, or fit their mental models.
A symptom of such mismatches is that TAP rules, which are developed independently with
specific goals in mind, could conflict with each other and cause deadlocks. We hypothesize
that the missing link between rules and human needs are the “intentions” of the actions. An
intention is defined to be a meaningful abstract objective that is associated with a rule. We
argue that every action performed in a rule-based automation corresponds to an “intentional
action”. If the intentions behind the actions are better captured, then the conflicts can be
reasoned and resolved in better accordance to the users’ needs.
In this study, we qualitatively studied intentions behind trigger-action rules and investigated
how the contexts influence the intentions in domestic IoT. We conducted a four-week
workshop to extract intentions by interpreting and sorting 253 different recipes which were
gathered from the IFTTT database. The results include: First, we derived a taxonomy of 8
primary intentions, 21 relevant topics and provided examples for each of them. Second, we
found that the contexts can meaningfully influence users’ intentions and the interpretation
of rules in a given situation. Finally, we provided several design implications that can help
designers to make use of intentions in designing domestic IoT system.

Nowadays, smart homes can be conveniently programmed by end-users. One of the most
popular ways is using trigger-action programming (TAP), which is based on rules in the
simple form of “IF a trigger occurs, THEN performs an action.” Programming smart homes
with TAP is generally considered to be easy to learn and very approachable by end-users.
However, this simplicity does not always fulfill the users’ needs, or fit their mental models.
A symptom of such mismatches is that TAP rules, which are developed independently with
specific goals in mind, could conflict with each other and cause deadlocks. We hypothesize
that the missing link between rules and human needs are the “intentions” of the actions. An
intention is defined to be a meaningful abstract objective that is associated with a rule. We
argue that every action performed in a rule-based automation corresponds to an “intentional
action”. If the intentions behind the actions are better captured, then the conflicts can be
reasoned and resolved in better accordance to the users’ needs.
In this study, we qualitatively studied intentions behind trigger-action rules and investigated
how the contexts influence the intentions in domestic IoT. We conducted a four-week
workshop to extract intentions by interpreting and sorting 253 different recipes which were
gathered from the IFTTT database. The results include: First, we derived a taxonomy of 8
primary intentions, 21 relevant topics and provided examples for each of them. Second, we
found that the contexts can meaningfully influence users’ intentions and the interpretation
of rules in a given situation. Finally, we provided several design implications that can help
designers to make use of intentions in designing domestic IoT system.

[1] S. C. Paternò F., A Design Space for End User Development in the Time of the Internet of Things, New Perspe. Springer, Cham, 2017.
[2] M. Walch, M. Rietzler, J. Greim, F. Schaub, B. Wiedersheim, and M. Weber, “homeBLOX: Making Home Automation Usable,” in Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, 2013, pp. 295–298.
[3] J. Lee, L. Garduño, E. Walker, and W. Burleson, “A Tangible Programming Tool for Creation of Context-aware Applications,” in Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2013, pp. 391–400.
[4] K. Tada, S. Takahashi, and B. Shizuki, “Smart Home Cards: Tangible Programming with Paper Cards,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, 2016, pp. 381–384.
[5] A. Vianello, Y. Florack, A. Bellucci, and G. Jacucci, “T4Tags 2.0: A Tangible System for Supporting Users’ Needs in the Domestic Environment,” in Proceedings of the TEI ’16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction - TEI ’16, 2016, pp. 38–43.
[6] A. K. Dey, T. Sohn, S. Streng, and J. Kodama, “iCAP: Interactive prototyping of context-aware applications,” in International Conference on Pervasive Computing, 2006, pp. 254–271.
[7] G. Ghiani, M. Manca, F. Paternò, and C. Santoro, “Personalization of Context-Dependent Applications Through Trigger-Action Rules,” ACM Trans. Comput. Interact., vol. 24, no. 2, pp. 14:1--14:33, 2017.
[8] A. A. Nacci et al., “BuildingRules: A Trigger-Action--Based System to Manage Complex Commercial Buildings,” ACM Trans. Cyber-Phys. Syst., vol. 2, no. 2, pp. 13:1--13:22, 2018.
[9] B. Ur, E. McManus, M. Pak Yong Ho, and M. L. Littman, “Practical trigger-action programming in the smart home,” in Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ’14, 2014, pp. 803–812.
[10] F. Corno, L. De Russis, and A. M. Roffarello, “A High-Level Approach Towards End User Development in the IoT,” in Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2017, pp. 1546–1552.
[11] F. Corno, L. D. Russis, and A. M. Roffarello, “A Semantic Web Approach to Simplifying Trigger-Action Programming in the IoT,” Computer (Long. Beach. Calif)., vol. 50, no. 11, pp. 18–24, 2017.
[12] W. Brackenbury et al., “How Users Interpret Bugs in Trigger-Action Programming,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, pp. 552:1--552:12.
[13] F. Corno, L. De Russis, and A. Monge Roffarello, “Empowering End Users in Debugging Trigger-Action Rules,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, pp. 388:1--388:13.
[14] C.-J. M. Liang et al., “Systematically Debugging IoT Control System Correctness for Building Automation,” in Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, 2016, pp. 133–142.
[15] M. Funk, L.-L. Chen, S.-W. Yang, and Y.-K. Chen, “Addressing the need to capture scenarios, intentions and preferences: Interactive intentional programming in the smart home,” Int. J. Des., vol. 12, no. 1, pp. 53–66, 2018.
[16] R. H. Jensen, Y. Strengers, J. Kjeldskov, L. Nicholls, and M. B. Skov, “Designing the Desirable Smart Home: A Study of Household Experiences and Energy Consumption Impacts,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018, pp. 4:1--4:14.
[17] S. Mennicken and E. M. Huang, “Hacking the Natural Habitat: An In-the-Wild Study of Smart Homes, Their Development, and the People Who Live in Them,” in Pervasive Computing, 2012, pp. 143–160.
[18] L. Takayama, C. Pantofaru, D. Robson, B. Soto, and M. Barry, “Making Technology Homey: Finding Sources of Satisfaction and Meaning in Home Automation,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 2012, pp. 511–520.
[19] X. Mi, F. Qian, Y. Zhang, and X. Wang, “An Empirical Characterization of IFTTT: Ecosystem, Usage, and Performance,” in Proceedings of the 2017 Internet Measurement Conference, 2017, pp. 398–404.
[20] M. Clark, M. W. Newman, and P. Dutta, “Devices and Data and Agents, Oh My: How Smart Home Abstractions Prime End-User Mental Models,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 1, no. 3, pp. 44:1--44:26, 2017.
[21] J. Brich, M. Walch, M. Rietzler, M. Weber, and F. Schaub, “Exploring end user programming needs in home automation,” ACM Trans. Comput. Interact., vol. 24, no. 2, p. 11, 2017.
[22] J. Fiorenza and A. Mariani, “Improving trigger action programming in smart buildings through suggestions based on behavioral graphs analysis,” 2015.
[23] S. Yarosh and P. Zave, “Locked or Not?: Mental Models of IoT Feature Interaction,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017, pp. 2993–2997.
[24] A. J. B. Brush, B. Lee, R. Mahajan, S. Agarwal, S. Saroiu, and C. Dixon, “Home Automation in the Wild: Challenges and Opportunities,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2011, pp. 2115–2124.
[25] M. Chan, D. Estève, C. Escriba, and E. Campo, “A review of smart homes---Present state and future challenges,” Comput. Methods Programs Biomed., vol. 91, no. 1, pp. 55–81, 2008.
[26] E. Costanza, J. E. Fischer, J. A. Colley, T. Rodden, S. D. Ramchurn, and N. R. Jennings, “Doing the Laundry with Agents: A Field Trial of a Future Smart Energy System in the Home,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014, pp. 813–822.
[27] S. Davidoff, M. K. Lee, C. Yiu, J. Zimmerman, and A. K. Dey, “Principles of Smart Home Control,” in Proceedings of the 8th International Conference on Ubiquitous Computing, 2006, pp. 19–34.
[28] E. Kaasinen, T. Kymäläinen, M. Niemelä, T. Olsson, M. Kanerva, and V. Ikonen, “A User-Centric View of Intelligent Environments: User Expectations, User Experience and User Role in Building Intelligent Environments,” Computers, vol. 2, no. 1, pp. 1–33, 2013.
[29] M. K. Lee, S. Davidoff, J. Zimmerman, and A. Dey, “Smart homes, families and control,” in Proceedings of Design & Emotion, 2006, vol. 2006.
[30] J. Huang and M. Cakmak, “Supporting mental model accuracy in trigger-action programming,” in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, pp. 215–225.
[31] S. Mennicken, J. Vermeulen, and E. M. Huang, “From Today’s Augmented Houses to Tomorrow’s Smart Homes: New Directions for Home Automation Research,” in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 105–115.
[32] K. Setiya, “Intention,” in The Stanford Encyclopedia of Philosophy, Fall 2018., E. N. Zalta, Ed. Metaphysics Research Lab, Stanford University, 2018.
[33] P. R. Cohen and H. J. Levesque, “Intention is choice with commitment,” Artif. Intell., vol. 42, no. 2–3, pp. 213–261, 1990.
[34] E. D. Mekler and K. Hornbæk, “A Framework for the Experience of Meaning in Human-Computer Interaction,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, pp. 225:1--225:15.
[35] P. Dourish, Where the action is: the foundations of embodied interaction. MIT press, 2004.
[36] B. Ur et al., “Trigger-Action Programming in the Wild: An Analysis of 200,000 IFTTT Recipes,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016, pp. 3227–3231.