With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
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Despite limited empirical support, vacations are marketed as beneficial for romantic partners. Using the self-expansion model as a foundation, we tested how self-expanding (e.g., novel, interesting, challenging) vacation experiences are associated with passion, physical intimacy, and relationship satisfaction. Study 1 (n = 238 partners) found that higher individual self-expanding experiences on vacations predicted higher post-vacation romantic passion and relationship satisfaction for couples traveling with their partners, but not those that did not travel together. Study 2 examined 102 romantic dyads that traveled together and found that higher self-expanding experiences on vacations predicted more post-vacation physical intimacy. Our findings advance self-expansion research and provide evidence for the tourism industry to design and promote self-expanding vacation experiences for couples seeking improved relationships and meaningful vacations.
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We used a validated agent-based model—Socio-Emotional CONcern DynamicS (SECONDS)—to model real-time playful interaction between a child diagnosed with Autism Spectrum Disorders (ASD) and its parent. SECONDS provides a real-time (second-by-second) virtual environment that could be used for clinical trials and testingprocess-orientedexplanationsofASDsymptomatology.Weconductednumerical experiments with SECONDS (1) for internal model validation comparing two parental behavioral strategies for stimulating social development in ASD (play-centered vs. initiative-centered) and (2) for empirical case-based model validation. We compared 2,000 simulated play sessions of two particular dyads with (second-by-second) time-series observations within 29 play sessions of a real parent-child dyad with ASD on six variables related to maintaining and initiating play. Overall, both simuladistributions. Given the idiosyncratic behaviors expected in ASD, the observed correspondence is non-trivial. Our results demonstrate the applicability of SECONDS to parent-child dyads in ASD. In the future, SECONDS could help design interventions for parental care in ASDted dyads provided a better fit to the observed dyad than reference null
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