An improved method for solving Hybrid Influence Diagrams

Date

2018-04-01

Author

Yet, Barbaros
Fenton, Norman
Constantinou, Anthony
Dementiev, Eugene

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

194
views

0
downloads

While decision trees are a popular formal and quantitative method for determining an optimal decision from a finite set of choices, for all but very simple problems they are computationally intractable. For this reason, Influence Diagrams (IDs) have been used as a more compact and efficient alternative. However, most algorithmic solutions assume that all chance variables are discrete, whereas in practice many are continuous. For such 'Hybrid' IDs (HIDs) the current-state-of-the-art algorithms suffer from various limitations on the kinds of inference that can be performed. This paper presents a novel method that overcomes a number of these limitations. The method solves a HID by transforming it to a Hybrid Bayesian Network (HBN) and carrying out inference on this HBN using Dynamic Discretization (DD). It generates a simplified decision tree from the propagated HBN to compute and present the optimal decisions under different decision scenarios. To provide satisfactory performance the method uses 'inconsistent evidence' to model functional and structural asymmetry. By using the entire marginal probability distribution of the continuous utility and chance nodes, rather than expected values alone, our method also enhances decision analysis by offering the possibility to consider additional statistics other than expected utility, such as measures of risk. We illustrate our method by using the oil wildcatter example and its variations with continuous nodes. We also use a financial score to combine risk and return measures, for illustration.

Subject Keywords

Dynamic Discretization, Bayesian networks;, Hybrid Influence Diagrams

URI

https://hdl.handle.net/11511/57058

Journal

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING

DOI

https://doi.org/10.1016/j.ijar.2018.01.006

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization Yet, Barbaros; Fenton, Norman; Neil, Martin (2018-01-01) In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This...
A visual interactive approach for scenario-based stochastic multi-objective problems and an application Balibek, E.; Köksalan, Mustafa Murat (2012-12-01) In many practical applications of stochastic programming, discretization of continuous random variables in the form of a scenario tree is required. In this paper, we deal with the randomness in scenario generation and present a visual interactive method for scenario-based stochastic multi-objective problems. The method relies on multi-variate statistical analysis of solutions obtained from a multi-objective stochastic problem to construct joint confidence regions for the objective function values. The decis...
An evolutionary metaheuristic for approximating preference-nondominated solutions Koekalan, Murat; Phelps, Selcen (Pamuk) (2007-03-01) We propose an evolutionary metaheuristic for approximating the preference-nondominated solutions of a decision maker in multiobjective combinatorial problems. The method starts out with some partial preference information provided by the decision maker, and utilizes an individualized fitness function to converge toward a representative set of solutions favored by the information at hand. The breadth of the set depends on the precision of the partial information available on the decision maker's preferences....
How to model mutually exclusive events based on independent causal pathways in Bayesian network models Fenton, Norman; Neil, Martin; Lagnado, David; Marsh, William; Yet, Barbaros; Constantinou, Anthony (2016-12-01) We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed 'solution', which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel an...
A neuro-fuzzy MAR algorithm for temporal rule-based systems Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04) This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...

Citation Formats

B. Yet, N. Fenton, A. Constantinou, and E. Dementiev, “An improved method for solving Hybrid Influence Diagrams,” INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, pp. 93–112, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57058.