A decision-theoretic representation of assistive interfaces
Résumé
Assistive interfaces, such as recommendation engines, adaptive systems, and intelligent assistants, span diverse methods and disci- plines 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 for- mulations; (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 prob- lems 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.
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