Assistive interfaces, such as recommendation engines, adaptive systems, and intelligent assistants, span diverse methods and disciplines but lack a shared conceptual foundation. This paper models assistance as sequential decision-making under uncertainty between two agents: the user and the assistant. The formalism allows casting assistance as an optimization problem and offers a rich but principled vocabulary to understand the dynamics of assistance. Drawing on Partially Observable Stochastic Games (POSGs) and related models, we: (1) motivate multi-agent over single-agent formulations; (2) adapt POSGs to HCI and clarify their tractability through reductions; (3) propose a two-agent sequential model that unambiguously defines concepts such as adaptation, augmentation, and delegation; (4) illustrate applicability through domain problems and examples; and (5) offer a supporting implementation via a library. These results warrant more attention on decision-theory as a principled yet actionable approach to assistive interfaces.

This paper proposes a new approach for online UI adaptation that aims to overcome the limitations of the most commonly used UI optimization method involving multiple objectives: weighted sum optimization. Weighted sums are highly sensitive to objective formulation, limiting the effectiveness of UI adaptations. We propose ParetoAdapt, an adaptation approach that uses online multi-objective optimization with a posteriori articulated preferences—that is, articulation of preferences after the optimization has concluded—to make UI adaptation robust to incomplete and inaccurate objective formulations. It offers users a flexible way to control adaptations by selecting from a set of Pareto optimal adaptation proposals and adjusting them to fit their needs. We showcase the feasibility and flexibility of ParetoAdapt by implementing an online layout adaptation system in a state-of-the-art 3D UI adaptation framework. We further evaluate its robustness and run-time in simulation-based experiments that allow us to systematically change the accuracy of the estimated user preferences. We conclude by discussing how our approach may impact the usability and practicality of online UI adaptations.