A decision support tool coupling a causal model and a multi-objective genetic algorithm

Authors:
Ivan Blecic;Arnaldo Cecchini;Giuseppe A. Trunfio
Affiliations:
Department of Architecture and Planning, University of Sassari, Italy;Department of Architecture and Planning, University of Sassari, Italy;Center of High-Performance Computing, University of Calabria, Rende (CS), Italy
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

Citing 5
Cited 0

Evidential reasoning using stochastic simulation of causal models

Artificial Intelligence
Partial abductive inference in Bayesian belief networks using a genetic algorithm

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The knowledge-driven causal models, implementing some inferential techniques, can prove useful in the assessment of effects of actions in contexts with complex probabilistic chains. Such exploratory tools can thus help in "forevisioning" of future scenarios, but frequently the inverse analysis is required, that is to say, given a desirable future scenario, to discover the "best" set of actions. This paper explores a case of such "future-retrovisioning", coupling a causal model with a multi-objective genetic algorithm. We show how a genetic algorithm is able to solve the strategy-selection problem, assisting the decision-maker in choosing an adequate strategy within the possibilities offered by the decision space. The paper outlines the general framework underlying an effective knowledge-based decision support system engineered as a software tool.