Abstract
A system deployed in the real world will need to handle uncertainty in its observations and interventions. For this, we present an approach to introduce uncertainty of state variables in causal reasoning using a constructivist AI architecture. Open questions of how noisy data can be handled and intervention uncertainty can be represented in a causal reasoning system will be addressed. In addition, we will show how handling uncertainty can improve a system’s planning and attention mechanisms. We present the reasoning process of the system, including reasoning over uncertainty, in the form of a feed-forward algorithm, highlighting how noisy data and beliefs of states can be included in the process of causal reasoning.
This work was funded in part by the Icelandic Research Fund (IRF) (grant number 228604-051) and a research grant from Cisco Systems, USA.
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See https://openaera.org – accessed Apr. 6, 2023.
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Eberding, L.M., Thórisson, K.R. (2023). Causal Reasoning over Probabilistic Uncertainty. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_8
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