Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/99343
Title: Multiple perspective dynamic decision making
Authors: Leong, T.Y. 
Keywords: Decision making
Knowledge representation
Multiple perspective reasoning
Probabilistic reasoning
Semi-Markov decision processes
Temporal reasoning
Issue Date: Oct-1998
Citation: Leong, T.Y. (1998-10). Multiple perspective dynamic decision making. Artificial Intelligence 105 (1-2) : 209-261. ScholarBank@NUS Repository.
Abstract: 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 paradigm 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.
Source Title: Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/99343
ISSN: 00043702
Appears in Collections:Staff Publications

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