Abstract
Humans engage in causal inference almost every day, however, the term ‘causation’ is still quite ambiguous, and few AI systems provide a comprehensive and satisfactory solution to causal inference. In this paper, we adopt the primary meaning of causation, i.e., prediction, and argue that in different contexts other demands are attached to it. We describe the approach of causal inference in NARS and present some working examples, both at the sensorimotor and abstract levels. The theoretical and practical consequences are quite different from traditional AI approaches.
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Notes
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\(\{T_1 \Rightarrow M, T_2 \Rightarrow M \} \vdash (T_1 \vee T_2) \Rightarrow M \langle F_{int}\rangle \) [15]. It is isomorphic in temporal inference.
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Acknowledgement
We sincerely thank Patrick Hammer’s implementation of NARS, which helped for the experimental part of this paper. We also appreciate the anonymous reviewers for their valuable feedback.
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Xu, B., Wang, P. (2024). Causal Inference in NARS. In: Thórisson, K.R., Isaev, P., Sheikhlar, A. (eds) Artificial General Intelligence. AGI 2024. Lecture Notes in Computer Science(), vol 14951. Springer, Cham. https://doi.org/10.1007/978-3-031-65572-2_22
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