A Decision Support Tool Coupling a Causal Model and a Multi-objective Genetic Algorithm

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 re...

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Bibliographic Details
Published inInnovations in Applied Artificial Intelligence pp. 628 - 637
Main Authors Blecic, Ivan, Cecchini, Arnaldo, Trunfio, Giuseppe A.
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Causal Model
Computer science; control theory; systems
Decision Space
Exact sciences and technology
Impact Factor
Multiobjective Optimization
Stochastic Simulation
Selection problem
Software tool
Probabilistic approach
Decision support system
Genetic algorithm
Multiobjective programming
Model driven architecture
Knowledge base
Causality
Artificial intelligence
Modeling
Inverse problem
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Summary: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.
ISBN:9783540265511
3540265511
ISSN:0302-9743
1611-3349
DOI:10.1007/11504894_88