A Distributed Artificial Network Solving Complex and Multiple Causal Associations

Authors:
Lotfi Ben Romdhane;Béchir Ayeb;Shengrui Wang
Affiliations:
University of Center, Faculty of Sciences/DSI, Monastir, Tunisia & University of Sherbrooke, Faculty of Sciences/DMI, Sherbrooke, Canada;University of Center, Faculty of Sciences/DSI, Monastir, Tunisia & University of Sherbrooke, Faculty of Sciences/DMI, Sherbrooke, Canada;University of Center, Faculty of Sciences/DSI, Monastir, Tunisia & University of Sherbrooke, Faculty of Sciences/DMI, Sherbrooke, Canada
Venue:
Applied Intelligence
Year:
2003

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Abstract

Causal reasoning (known also as abduction) is a hard task that cognitive agents perform reliably and quickly. A particular class of causal reasoning that raises several difficulties is the cancellation class. Cancellation occurs when a set of causes (hypotheses) cancel each other's explanation with respect to a given effect (observation). For example, a cloudy sky may suggest a rainy weather; whereas a shiny sky may suggest the absence of rain. In the current paper, we extend a recent neural model to handle cancellation interactions. We conduct a sensitivity analysis of this proposal on ad hoc problems put at extreme cases. Finally, we test the model on a large database and propose objective criteria to quantitatively evaluate its performance. Simulation results are very satisfactory and should encourage research.