When actions have consequences: empirically based decision making for intelligent user interfaces

One feature of intelligent user interfaces is an ability to make decisions that take into account a variety of factors, some of which may depend on the current situation. This article focuses on one general approach to such decision making: predict the consequences of possible system actions on the basis of prior empirical learning, and evaluate the possible actions, taking into account situation-dependent priorities and the tradeoffs between the consequences. This decision-theoretic approach is illustrated in detail with reference to an example decision problem, for which models for decision making were learned from experimental data. It is shown how influence diagrams and methods of decision-theoretic planning can be applied to arrive at empirically well-founded decisions. This paradigm is then compared with two other paradigms that are often employed in intelligent user interfaces. Finally, various possible ways of learning (or otherwise deriving) suitable decision-theoretic models are discussed.

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