This paper introduces an automated decision-making framework for providing controlled agent behavior in systems dealing with human behavior-change. Controlled behavior in such settings is important in order to reduce unexpected side-effects of a system’s actions. The general structure of the framework is based on a psychological theory, the Theory of Planned Behavior (TPB), capturing causes to human motivational states, which enables reasoning about dynamics of human motivation. The framework consists of two main components: 1) an ontological knowledge-base that models an individual’s behavioral challenges to infer motivation states and 2) a transition system that, in a given motivation state, decides on motivational support, resulting in transitions between motivational states. The system generates plans (sequences of actions) for an agent to facilitate behavior change. A particular use-case is modeled regarding children with Autism Spectrum Conditions (ASC) who commonly experience difficulties in everyday social situations. An evaluation of a proof-of-concept prototype is performed that presents consistencies between ASC experts’ suggestions and plans generated by the system.
Icek Ajzen et al. The theory of planned behavior, Organizational Behavior and Human Decision Processes50(2) (1991), 179–211.
2.
Timothy W Bickmore, Suzanne E Mitchell, Brian W Jack, Michael K Paasche-Orlow, Laura M Pfeifer and Julie O’Donnell, Response to a relational agent by hospital patients with depressive symptoms, Interacting with Computers22(4) (2010), 289–298.
3.
Justin Blount and Michael Gelfond, Reasoning about the intentions of agents. In Logic Programs, Norms and Action, pages 147–171. Springer, 2012.
4.
Andreas Brannstrom and Juan Carlos Nieves, Modelling human mental-states in an action language following the theory of planned behavior. In 14th Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP)@ ICLP, Virtual, September 20-27, 2021.
5.
Andreas Brannstrom and Juan Carlos Nieves, Emotional reasoning in an action language for emotion-aware planning. In International Conference on Logic Programming and Nonmonotonic Reasoning, pages 103–116. Springer, 2022.
6.
Andreas Brannstrom and Juan Carlos Nieves, A framework for developing interactive intelligent systems in unity. Proceedings of the Engineering Multi-Agent Systems (EMAS 2022), Virtual Event, pages 9–10, 2022.
7.
Andreas Brännström and Juan Carlos Nieves, Multi-mind dynamics in intentional agents. In 2nd International Workshop on Hybrid models for Coupling Deductive and Inductive Reasoning (HYDRA)@ ECAI-23, Kraków, Poland, September 30-October 4, 2023, 2023.
8.
Andreas Brännström, Joel Wester and Juan Carlos Nieves, A formal understanding of computational empathy in interactive agents, Cognitive Systems Research (2023), 101203.
9.
Gerhard Brewka and Thomas Eiter, Equilibria in heterogeneous nonmonotonic multi-context systems. In AAAI7 (2007), 385–390.
10.
Michelle Nicole Burns, Mark Begale, Jennifer Duffecy, Darren Gergle, Chris J Karr, Emily Giangrande and David C Mohr, Harnessing context sensing to develop a mobile intervention for depression, Journal of medical Internet Research13(3) (2011), e55.
11.
Tathagata Chakraborti, Foundations of Human-Aware Planning-A Tale of Three Models. PhD thesis, Arizona State University, 2018.
12.
Philip R Cohen, Back to the future for dialogue research: A position paper. arXiv preprint arXiv:1812.01144, 2018.
13.
Thomas Eiter, Wolfgang Faber, Nicola Leone, Gerald Pfeifer and Axel Polleres, A logic programming approach to knowledge-state planning, ii: The dlvk system, Artificial Intelligence144(1–2) (2003), 157–211.
14.
Chris Frith and Uta Frith, Theory of mind, Current Biology15(17) (2005), R644–R645.
15.
Michael Gelfond and Vladimir Lifschitz, Action languages. 1998.
16.
Nicola Guarino, Daniel Oberle and Steffen Staab, What is an ontology? In Handbook on ontologies, pages 1–17. Springer, 2009.
17.
Ian Horrocks, Peter F Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, Mike Dean, et al. Swrl: A semantic web rule language combining owl and ruleml, W3C Member Submission21(79) (2004), 1–31.
18.
Vladimir Lifschitz, Answer set programming, Springer International Publishing, 2019.
19.
Judith Masthoff, Floriana Grasso and Jaap Ham, Preface to the special issue on personalization and behavior change, User Modeling and User-Adapted Interaction24(5) (2014), 345–350.
20.
David C Mohr, Michelle Nicole Burns, Stephen M Schueller, Gregory Clarke and Michael Klinkman, Behavioral intervention technologies: evidence review and recommendations for future research in mental health, General Hospital Psychiatry35(4) (2013), 332–338.
21.
Christian Muise, Tathagata Chakraborti, Shubham Agarwal, Ondrej Bajgar, Arunima Chaudhary, Luis A Lastras-Montano, Josef Ondrej, Miroslav Vodolan and Charlie Wiecha, Planning for goal-oriented dialogue systems, arXiv preprint arXiv:1910.08137, 2019.
22.
Inbal Nahum-Shani, Shawna N Smith, Bonnie J Spring, Linda M Collins, Katie Witkiewitz, Ambuj Tewari and Susan A Murphy, Just-in-time adaptive interventions (jitais) in mobile health: key components and design principles for ongoing health behavior support, Annals of Behavioral Medicine52(6) (2018), 446–462.
23.
Miquel Ramirez and Hector Geffner, Plan recognition as planning. In Proceedings of the 21st international joint conference on Artifical intelligence. Morgan Kaufmann Publishers Inc, pages 1778–1783, 2009.
24.
Nigel Shadbolt, Paul R Smart, J.R. Wilson and S. Sharples, Knowledge elicitation, Evaluation of Human Work (2015), 163–200.
25.
Maayan Shvo and Sheila A McIlraith, Active goal recognition. In AAAI, pages 9957–9966, 2020.
26.
Emily Simonoff, Andrew Pickles, Tony Charman, Susie Chandler, Tom Loucas and Gillian Baird, Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample, Journal of the American Academy of Child & Adolescent Psychiatry47(8) (2008), 921–929.
27.
Donna Spruijt-Metz, Eric Hekler, Niilo Saranummi, Stephen Intille, Ilkka Korhonen, Wendy Nilsen, Daniel E Rivera, Bonnie Spring, Susan Michie, David A Asch, et al. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research, Translational Behavioral Medicine5(3) (2015), 335–346.
28.
Fawad Taj, Michel CA Klein and Aart van Halteren, Digital health behavior change technology: Bibliometric andscoping review of two decades of research, JMIR mHealth and uHealth7(12) (2019), e13311.
29.
Thomas Webb, Judith Joseph, Lucy Yardley and Susan Michie, Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy, Journal of Medical Internet Research12(1) (2010), e1376.
