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.
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Romdhane, L.B., Ayeb, B. & Wang, S. A Distributed Artificial Network Solving Complex and Multiple Causal Associations. Applied Intelligence 19, 189–207 (2003). https://doi.org/10.1023/A:1026010008129
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DOI: https://doi.org/10.1023/A:1026010008129