Decision making often involves deliberations in different perspectives. Distinct perspectives or views support knowledge acquisition and representation suitable for different types or stages of inference in the same discourse. This work presents a general paradigm for multiple perspective decision making over time and under uncertainty. Based on a unifying task definition and a common vocabulary for the relevant decision problems, this new paradig m balances the trade-off between model transparency and solution efficiency in current decision frameworks. The new paradigm motivates the design of DynaMoL (Dynamic decision Modeling Language), a general language for modeling and solving dynamic decision problems. The DynaMoL framework differentiates inferential and representational support for the modeling task from the solution or computation task. The dynamic decision grammar defines an extensible decision ontology and supports complex problem specification with multiple interfaces. The graphical presentation convention governs parameter visualization in multiple perspectives. The mathematical representation as semi-Markov decision process facilitates formal model analysis and admits multiple solution methods. A set of general translation techniques is devised to manage the different perspectives and representations of the decision parameters and constraints. DynaMoL has been evaluated on a prototype implementation, via some comprehensive case studies in medicine. The results demonstrate practical promise of the framework. © 1998 Elsevier Science B.V. All rights reserved.
Leong, T. Y. (1998). Multiple perspective dynamic decision making. Artificial Intelligence, 105(1–2), 209–261. https://doi.org/10.1016/s0004-3702(98)00082-4