A distributed artificial network solving complex and multiple causal associations

摘要

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. [References: 23]