16 research outputs found
New Graphical Model for Computing Optimistic Decisions in Possibility Theory Framework
This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic networks: the first one expresses agent's knowledge while the second one encodes agent's preferences representing a qualitative utility. We then propose an efficient algorithm for computing optimistic optimal decisions using our new model for representing possibilistic decision making under uncertainty. We show that the computation of optimal decisions comes down to compute a normalization degree of the junction tree associated with the graph resulting from the fusion of agent's beliefs and preferences. This paper also proposes an alternative way for computing optimal optimistic decisions. The idea is to transform the two possibilistic networks into two equivalent possibilistic logic knowledge bases, one representing agent's knowledge and the other represents agent's preferences. We show that computing an optimal optimistic decision comes down to compute the inconsistency degree of the union of the two possibilistic bases augmented with a given decision
A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data
A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis
Decision Making under Uncertainty through Extending Influence Diagrams with Interval-valued Parameters
Influence Diagrams (IDs) are one of the most commonly used graphical
and mathematical decision models for reasoning under uncertainty. In conventional
IDs, both probabilities representing beliefs and utilities representing preferences of
decision makers are precise point-valued parameters. However, it is usually difficult
or even impossible to directly provide such parameters. In this paper, we extend
conventional IDs to allow IDs with interval-valued parameters (IIDs), and develop a
counterpart method of Copper’s evaluation method to evaluate IIDs. IIDs avoid the
difficulties attached to the specification of precise parameters and provide the
capability to model decision making processes in a situation that the precise
parameters cannot be specified. The counterpart method to Copper’s evaluation
method reduces the evaluation of IIDs into inference problems of IBNs. An algorithm
based on the approximate inference of IBNs is proposed, extensive experiments are
conducted. The experimental results indicate that the proposed algorithm can find the
optimal strategies effectively in IIDs, and the interval-valued expected utilities
obtained by proposed algorithm are contained in those obtained by exact evaluating
algorithms
Automated clinical decision model construction from knowledge-based GLIF guideline models
Master'sMASTER OF ENGINEERIN
Graphical modeling of asymmetric games and value of information in multi-agent decision systems
Master'sMASTER OF ENGINEERIN